From 579bbc97352f4c81ef61903927b6df9ccc88cd02 Mon Sep 17 00:00:00 2001 From: Krishika <203898617+krishika08@users.noreply.github.com> Date: Tue, 10 Nov 2026 18:16:36 +0530 Subject: [PATCH] Prepare RepoNeedle 1.2.0 release candidate Co-Authored-By: Claude Opus 5.5 --- .github/workflows/ci.yml | 44 + .github/workflows/release.yml | 65 +- CLAIMS_AND_EVIDENCE.md | 34 +- FINAL_RELEASE_AUDIT.md | 3 + HF_RELEASE_CHECKLIST.md | 3 + KNOWN_LIMITATIONS.md | 111 +- README.md | 449 ++- REPONEEDLE_V1_FINAL_DELIVERY.md | 4 + benchmarks/render_product_aligned_tables.py | 52 + benchmarks/run_ablations.py | 7 +- .../run_arb_comprehensive_validation.py | 10 +- benchmarks/run_arb_current_engine.py | 224 ++ benchmarks/run_arb_evaluator_audit.py | 364 +++ benchmarks/run_arb_product_aligned_eval.py | 492 ++++ benchmarks/run_complete_arb_evaluation.py | 9 +- benchmarks/run_doc_filter_validation.py | 437 +++ benchmarks/run_lexical_forensics.py | 506 ++++ benchmarks/runner.py | 7 +- docs/ANNOUNCEMENT.md | 57 +- docs/BENCHMARK_CONFIGURATION.md | 165 ++ docs/MCP_INTEGRATION.md | 335 +++ docs/ROADMAP.md | 71 +- docs/v1.1_change_impact_release.md | 2 +- pyproject.toml | 12 + reponeedle/__init__.py | 7 +- reponeedle/cli.py | 301 +- reponeedle/core/abstention.py | 15 +- reponeedle/engine.py | 226 +- reponeedle/mcp/__init__.py | 9 + reponeedle/mcp/__main__.py | 9 + reponeedle/mcp/config.py | 69 + reponeedle/mcp/server.py | 215 ++ reponeedle/mcp/tools.py | 226 ++ reponeedle/models.py | 55 + reports/arb_v2_current_engine/SUMMARY.json | 338 +++ .../arb_v2_current_engine/shard_0_of_3.jsonl | 121 + .../arb_v2_current_engine/shard_1_of_3.jsonl | 118 + .../arb_v2_current_engine/shard_2_of_3.jsonl | 106 + .../phase0_evaluator_audit/PHASE0_REPORT.md | 186 ++ .../phase0_evaluator_audit/SUMMARY.json | 563 ++++ .../reconstructed_docs/traces.jsonl | 30 + .../reconstructed_docs/traces.jsonl | 29 + .../reconstructed_docs/traces.jsonl | 21 + .../doc_distractor/comment2context.jsonl | 34 + .../comment2context_part0.jsonl | 18 + .../comment2context_part1.jsonl | 11 + .../comment2context_part2.jsonl | 8 + .../comment2context_part3.jsonl | 9 + .../reconstructed_docs/shard_0_of_3.jsonl | 67 + .../reconstructed_docs/shard_1_of_3.jsonl | 71 + .../reconstructed_docs/shard_2_of_3.jsonl | 59 + .../reconstructed_docs/traces.jsonl | 4 + .../phase0_evaluator_audit/shard_0_of_3.jsonl | 121 + .../phase0_evaluator_audit/shard_1_of_3.jsonl | 118 + .../phase0_evaluator_audit/shard_2_of_3.jsonl | 106 + .../phase0_evaluator_audit/traces.jsonl | 4 + .../REPORT.md | 122 + .../REPRODUCE.md | 29 + .../TABLES.md | 231 ++ .../aggregate.json | 352 +++ .../confidence_intervals.json | 1512 ++++++++++ .../config.json | 64 + .../determinism_check.json | 6 + .../fidelity.json | 22 + .../per_query/shard_0_of_4.jsonl | 91 + .../per_query/shard_1_of_4.jsonl | 84 + .../per_query/shard_2_of_4.jsonl | 94 + .../per_query/shard_3_of_4.jsonl | 76 + .../rankings/shard_0_of_4.jsonl | 91 + .../rankings/shard_1_of_4.jsonl | 84 + .../rankings/shard_2_of_4.jsonl | 94 + .../rankings/shard_3_of_4.jsonl | 76 + .../run_metadata.json | 9 + .../phase2_documentation_filter/REPORT.md | 157 ++ .../dev/shard_0_of_4.jsonl | 45 + .../dev/shard_1_of_4.jsonl | 43 + .../dev/shard_2_of_4.jsonl | 41 + .../dev/shard_3_of_4.jsonl | 37 + .../dev_results.json | 1203 ++++++++ .../false_negative_examples.json | 13 + .../phase2_documentation_filter/protocol.json | 77 + .../protocol.sha256 | 1 + .../run_metadata.json | 7 + .../selected_filter_config.json | 21 + .../selected_filter_config.sha256 | 1 + .../phase2_documentation_filter/split.json | 274 ++ .../phase2_documentation_filter/split.sha256 | 1 + .../test/shard_0_of_4.jsonl | 48 + .../test/shard_1_of_4.jsonl | 44 + .../test/shard_2_of_4.jsonl | 43 + .../test/shard_3_of_4.jsonl | 44 + .../test_results.json | 502 ++++ .../phase3_lexical_retrieval/REPORT.md | 107 + .../dev_selection.json | 19 + .../dev_selection.sha256 | 1 + .../forensics_query_and_length.json | 77 + .../hybrid/shard_0_of_4.jsonl | 45 + .../hybrid/shard_1_of_4.jsonl | 43 + .../hybrid/shard_2_of_4.jsonl | 41 + .../hybrid/shard_3_of_4.jsonl | 37 + .../hybrid_candidates.json | 11 + .../hybrid_candidates.sha256 | 1 + .../lexical/shard_0_of_4.jsonl | 45 + .../lexical/shard_1_of_4.jsonl | 43 + .../lexical/shard_2_of_4.jsonl | 41 + .../lexical/shard_3_of_4.jsonl | 37 + .../phase3_lexical_retrieval/protocol.json | 50 + .../phase3_lexical_retrieval/protocol.sha256 | 1 + .../stage1_results.json | 2448 +++++++++++++++++ .../stage2_results.json | 788 ++++++ .../PROPOSED_CHANGELOG_1.2.0.md | 69 + reports/release_audit_1.2.0/RELEASE_AUDIT.md | 141 + research/stage3_6/REPRODUCIBILITY_REPORT.md | 8 + .../stage3_6/scripts/evaluate_abstention.py | 13 +- research/stage3_6/scripts/test_abstention.py | 11 +- scripts/verify_abstention_reproducibility.py | 224 ++ tests/conftest.py | 23 + tests/test_abstention_opt_in.py | 371 +++ tests/test_cli_anchors.py | 213 ++ tests/test_cli_json.py | 203 ++ tests/test_doc_filter_validation.py | 129 + tests/test_documentation_consistency.py | 265 ++ tests/test_engine.py | 9 +- tests/test_indexing_diagnostics.py | 332 +++ tests/test_indexing_security.py | 194 ++ tests/test_lexical_forensics.py | 98 + tests/test_mcp_optional_dependency.py | 32 + tests/test_mcp_server.py | 182 ++ tests/test_mcp_tools.py | 223 ++ tests/test_product_aligned_eval.py | 105 + tests/test_release_compat.py | 121 + 131 files changed, 18940 insertions(+), 237 deletions(-) create mode 100644 benchmarks/render_product_aligned_tables.py create mode 100644 benchmarks/run_arb_current_engine.py create mode 100644 benchmarks/run_arb_evaluator_audit.py create mode 100644 benchmarks/run_arb_product_aligned_eval.py create mode 100644 benchmarks/run_doc_filter_validation.py create mode 100644 benchmarks/run_lexical_forensics.py create mode 100644 docs/BENCHMARK_CONFIGURATION.md create mode 100644 docs/MCP_INTEGRATION.md create mode 100644 reponeedle/mcp/__init__.py create mode 100644 reponeedle/mcp/__main__.py create mode 100644 reponeedle/mcp/config.py create mode 100644 reponeedle/mcp/server.py create mode 100644 reponeedle/mcp/tools.py create mode 100644 reports/arb_v2_current_engine/SUMMARY.json create mode 100644 reports/arb_v2_current_engine/shard_0_of_3.jsonl create mode 100644 reports/arb_v2_current_engine/shard_1_of_3.jsonl create mode 100644 reports/arb_v2_current_engine/shard_2_of_3.jsonl create mode 100644 reports/experimental/phase0_evaluator_audit/PHASE0_REPORT.md create mode 100644 reports/experimental/phase0_evaluator_audit/SUMMARY.json create mode 100644 reports/experimental/phase0_evaluator_audit/c2c_fast_0/reconstructed_docs/traces.jsonl create mode 100644 reports/experimental/phase0_evaluator_audit/c2c_fast_1/reconstructed_docs/traces.jsonl create mode 100644 reports/experimental/phase0_evaluator_audit/c2c_fast_2/reconstructed_docs/traces.jsonl create mode 100644 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100644 reports/experimental/phase0_evaluator_audit/shard_1_of_3.jsonl create mode 100644 reports/experimental/phase0_evaluator_audit/shard_2_of_3.jsonl create mode 100644 reports/experimental/phase0_evaluator_audit/traces.jsonl create mode 100644 reports/experimental/phase1_product_aligned_evaluation/REPORT.md create mode 100644 reports/experimental/phase1_product_aligned_evaluation/REPRODUCE.md create mode 100644 reports/experimental/phase1_product_aligned_evaluation/TABLES.md create mode 100644 reports/experimental/phase1_product_aligned_evaluation/aggregate.json create mode 100644 reports/experimental/phase1_product_aligned_evaluation/confidence_intervals.json create mode 100644 reports/experimental/phase1_product_aligned_evaluation/config.json create mode 100644 reports/experimental/phase1_product_aligned_evaluation/determinism_check.json create mode 100644 reports/experimental/phase1_product_aligned_evaluation/fidelity.json create mode 100644 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reports/experimental/phase3_lexical_retrieval/protocol.sha256 create mode 100644 reports/experimental/phase3_lexical_retrieval/stage1_results.json create mode 100644 reports/experimental/phase3_lexical_retrieval/stage2_results.json create mode 100644 reports/release_audit_1.2.0/PROPOSED_CHANGELOG_1.2.0.md create mode 100644 reports/release_audit_1.2.0/RELEASE_AUDIT.md create mode 100644 scripts/verify_abstention_reproducibility.py create mode 100644 tests/conftest.py create mode 100644 tests/test_abstention_opt_in.py create mode 100644 tests/test_cli_anchors.py create mode 100644 tests/test_cli_json.py create mode 100644 tests/test_doc_filter_validation.py create mode 100644 tests/test_documentation_consistency.py create mode 100644 tests/test_indexing_diagnostics.py create mode 100644 tests/test_indexing_security.py create mode 100644 tests/test_lexical_forensics.py create mode 100644 tests/test_mcp_optional_dependency.py create mode 100644 tests/test_mcp_server.py create mode 100644 tests/test_mcp_tools.py create mode 100644 tests/test_product_aligned_eval.py create mode 100644 tests/test_release_compat.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index b6c0c49..dc2e8a6 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -60,6 +60,50 @@ jobs: run: | reponeedle "test query" --repo . --budget 500 --ranker change_impact + mcp-security-linux: + name: MCP & Indexing Security (Linux, Python 3.12, skips fail) + runs-on: ubuntu-latest + permissions: + contents: read + steps: + - name: Checkout code + uses: actions/checkout@v4 + + - name: Set up Python 3.12 + uses: actions/setup-python@v5 + with: + python-version: "3.12" + cache: "pip" + + - name: Install package with development and MCP extras + run: | + python -m pip install --upgrade pip setuptools wheel + pip install -e ".[dev,mcp]" + + - name: Verify optional MCP dependencies are importable + run: | + python -c "import mcp, pytest_asyncio; print('mcp and pytest-asyncio available')" + + - name: Run MCP, symlink-security and compatibility suites (any skip fails the job) + env: + REPONEEDLE_FAIL_ON_SKIP: "1" + run: | + pytest -q -rs \ + tests/test_mcp_server.py \ + tests/test_mcp_tools.py \ + tests/test_mcp_optional_dependency.py \ + tests/test_indexing_security.py \ + tests/test_release_compat.py \ + tests/test_abstention_opt_in.py \ + tests/test_indexing_diagnostics.py \ + tests/test_documentation_consistency.py + + - name: MCP STDIO smoke test (initialize; reports RepoNeedle version) + run: | + printf '%s\n' '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"ci","version":"1"}}}' \ + | timeout 120 python -m reponeedle.mcp --repo . 2>/dev/null | head -n 1 > init.json + python -c "import json, reponeedle; r = json.load(open('init.json')); info = r['result']['serverInfo']; assert info == {'name': 'reponeedle', 'version': reponeedle.__version__}, info; print('serverInfo OK:', info)" + research-parity: name: Research Benchmark Parity (Python 3.11) runs-on: ubuntu-latest diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 14f6c53..08acb28 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -9,8 +9,62 @@ permissions: contents: read jobs: + validate: + name: Validate Version & Run Release Test Suite + runs-on: ubuntu-latest + # Test job: read-only, no OIDC token, no publishing credentials. + permissions: + contents: read + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + cache: "pip" + + - name: Install package with development and MCP extras + run: | + python -m pip install --upgrade pip setuptools wheel + pip install -e ".[dev,mcp]" + + - name: Verify tag, pyproject version and package __version__ match + env: + TAG_NAME: ${{ github.ref_name }} + run: | + python - <<'EOF' + import os, sys, tomllib + import reponeedle + tag = os.environ["TAG_NAME"] + tag_version = tag[1:] if tag.startswith("v") else tag + project_version = tomllib.load(open("pyproject.toml", "rb"))["project"]["version"] + package_version = reponeedle.__version__ + print(f"tag={tag} tag_version={tag_version} pyproject={project_version} __version__={package_version}") + if not (tag_version == project_version == package_version): + sys.exit("Version mismatch: refusing to release. Tag, pyproject.toml and reponeedle.__version__ must agree.") + EOF + + - name: Run full test suite + run: | + pytest -q -rs + + - name: Run MCP and security suites (any skip fails the release) + env: + REPONEEDLE_FAIL_ON_SKIP: "1" + run: | + pytest -q -rs \ + tests/test_mcp_server.py \ + tests/test_mcp_tools.py \ + tests/test_mcp_optional_dependency.py \ + tests/test_indexing_security.py \ + tests/test_release_compat.py + build: name: Build & Validate Distributions + needs: validate runs-on: ubuntu-latest permissions: contents: read @@ -40,6 +94,14 @@ jobs: run: | python -m twine check --strict dist/* + - name: Verify built artifacts carry the tagged version + env: + TAG_NAME: ${{ github.ref_name }} + run: | + VERSION="${TAG_NAME#v}" + test -f "dist/reponeedle-${VERSION}-py3-none-any.whl" || { echo "Wheel for ${VERSION} not found"; ls dist; exit 1; } + test -f "dist/reponeedle-${VERSION}.tar.gz" || { echo "sdist for ${VERSION} not found"; ls dist; exit 1; } + - name: Generate and record SHA-256 checksums run: | mkdir -p checksums @@ -72,7 +134,8 @@ jobs: publish: name: Publish to PyPI (OIDC Trusted Publishing) - needs: build + # Only after version validation, the full test suite and a verified build have succeeded. + needs: [validate, build] runs-on: ubuntu-latest environment: name: pypi diff --git a/CLAIMS_AND_EVIDENCE.md b/CLAIMS_AND_EVIDENCE.md index b7344d1..15c7ffe 100644 --- a/CLAIMS_AND_EVIDENCE.md +++ b/CLAIMS_AND_EVIDENCE.md @@ -1,6 +1,6 @@ # CLAIMS AND EVIDENCE: SCIENTIFIC RECONCILIATION FOR REPONEEDLE -**Version**: 0.1.0 Research Preview +**Version**: Evidence originally compiled for the 0.1.0 Research Preview. Claims C11–C13 and corrections to C2, C4 and C6 were added on 2026-11-09 and cover released 1.1.0 and the unreleased development branch planned for 1.2.0. **Benchmark Suite**: Official Agent Retrieval Bench (ARB v2, $N=427$ total samples, $N=345$ positive retrieval samples across 25 repositories and 271 unique repository snapshots) **Evaluator**: `agent_retrieval_bench` v0.2.1, Commit `9e4d68937f29` **Evaluation Date**: 2026-10-28 @@ -14,7 +14,7 @@ This document provides a line-by-line verification of all scientific, empirical, | # | Claim Description | Status | Empirical Verdict | | :-: | :--- | :---: | :--- | | **C1** | RepoNeedle significantly outperforms BM25 on positive retrieval MRR on ARB v2 | **SUPPORTED** | Statistically significant ($+50.5\%$ relative gain, $p = 2.65\times 10^{-6}$) | -| **C2** | RepoNeedle significantly outperforms Lexical baseline on positive retrieval MRR | **SUPPORTED** | Statistically significant ($+45.3\%$ relative gain, $p = 8.17\times 10^{-6}$) | +| **C2** | RepoNeedle significantly outperforms Lexical baseline on positive retrieval MRR | **SUPPORTED (mean MRR; see caveat)** | Paired $t$-test significant ($+45.3\%$ relative gain, $p = 8.17\times 10^{-6}$), but Wilcoxon signed-rank is not ($p = 0.1215$) and RepoNeedle wins fewer queries than it loses (133 / 29 / 183) | | **C3** | RepoNeedle outperforms RepoMap overall on ARB v2 | **REFUTED / NOT SUPPORTED** | Parity on MRR ($p = 0.4089$); RepoMap dominates deep recall (R@20 $+47.4\%$) | | **C4** | RepoNeedle is superior for regression test discovery (`code2test`) | **SUPPORTED** | Strongest track ($+151.7\%$ vs BM25, $+37.0\%$ vs RepoMap on MRR) | | **C5** | RepoNeedle outperforms classical baselines on issue comments (`comment2context`) | **REFUTED / NOT SUPPORTED** | BM25 achieves higher MRR ($0.1974$ vs $0.1708$, $-13.5\%$ deficit for RepoNeedle) | @@ -23,6 +23,25 @@ This document provides a line-by-line verification of all scientific, empirical, | **C8** | Budget knapsack packing maximizes context yield under strict token limits | **SUPPORTED** | $+20.4\%$ advantage over RepoMap at BCY@4k ($0.2436$ vs $0.2024$) | | **C9** | Structural skeletons reduce token payload without sacrificing signatures | **SUPPORTED** | $52.8\%$ to $90.4\%$ measured BPE token compression across 4 languages | | **C10**| System is lightweight and runs purely on commodity CPU without GPUs | **SUPPORTED** | $317.7\text{ ms}$ mean query latency; zero PyTorch or CUDA dependencies | +| **C11**| "100% selective abstention accuracy" (v0.1.0 announcement) | **RETRACTED** | Based on 3 synthetic samples from a stale result file; current code scores 2/3 on that set and 0/82 on ARB v2 | +| **C12**| "100% test coverage across core modules" (v0.1.0 roadmap) | **REFUTED** | Measured line coverage of the `reponeedle` package: 87% (2026-11-09) | +| **C13**| The MCP server works with Claude Code / Cursor | **NOT VERIFIED (protocol level only)** | Verified with the MCP Python SDK and a JSON-RPC STDIO handshake; no live client session recorded | + +### Re-check on the Current Engine (2026-11-09) + +The ARB-derived evidence for C1–C8 and C10 was measured on 2026-10-28 with the engine at commit `8e00425` (tag `v0.1.0-baseline`). Engine changes made before the 1.1.0 release altered the rankings, so the ARB v2 positive split was re-measured with the current engine (1.1.0, identical to the development branch). Method and full tables: [docs/BENCHMARK_CONFIGURATION.md §7](docs/BENCHMARK_CONFIGURATION.md#7-verification-performed-2026-11-09). + +| # | Status on the current engine | Current evidence | +| :-: | :---: | :--- | +| C1 | **SUPPORTED** | MRR 0.2153 vs BM25 0.1520 (+41.6%); repository-clustered 95% CI of the difference [+0.0111, +0.0980] | +| C2 | **SUPPORTED (mean MRR; same caveat)** | +36.8% vs Lexical, clustered CI [+0.0180, +0.0933]; wins / ties / losses 118 / 24 / 203 | +| C3 | **REFUTED / NOT SUPPORTED** | Parity on MRR (+0.0013, clustered CI [−0.0597, +0.0517]); RepoMap Recall@20 0.6306 vs 0.3842 | +| C4 | **SUPPORTED** | `code2test` MRR 0.2546 vs BM25 0.1057 (+140.8%) and RepoMap 0.1942 (+31.0%) | +| C5 | **REFUTED / NOT SUPPORTED** | `comment2context` MRR 0.1658 vs BM25 0.1974 (−16.0%) | +| C6 | **REFUTED / NOT SUPPORTED** | 0/82 abstained with explicit opt-in (unchanged) | +| C7 | **NOT RE-MEASURED** | Router ablation was not re-run on the current engine | +| C8 | **SUPPORTED (no CI)** | BCY@4k 0.2400 vs RepoMap 0.2024 (+18.5%); no confidence interval computed for BCY | +| C10 | **SUPPORTED (latency not comparable)** | CPU-only dependencies unchanged; re-measurement latency was taken with three concurrent shards and is not comparable to the published figures | --- @@ -53,6 +72,7 @@ This document provides a line-by-line verification of all scientific, empirical, - Standard Error: `0.0157` - Repository-Clustered 95% Bootstrap CI: `[+0.0307, +0.1029]` - Paired Student's $t$-test: $t = 4.529$, $p = 8.17 \times 10^{-6}$ ($p < 0.001$) + - *Caveat (added 2026-11-09)*: the Wilcoxon signed-rank test is **not** significant ($W = 22521.5$, $p = 0.1215$), and per-query wins / ties / losses are 133 / 29 / 183. The mean gain comes from fewer, larger wins rather than consistent per-query improvement. The README's significance column reflects the $t$-test and repository-clustered bootstrap. ### Claim 3: "RepoNeedle outperforms RepoMap overall on ARB v2" - **Status**: **REFUTED / NOT SUPPORTED** @@ -100,7 +120,7 @@ This document provides a line-by-line verification of all scientific, empirical, - Deficit vs BM25: $\frac{0.1708 - 0.1974}{0.1974} \times 100\% = \mathbf{-13.48\%}$ - Recall@20 Deficit: BM25 `0.5292` vs RepoNeedle `0.2083` ($-60.6\%$ deficit) - BCY@8k Deficit: BM25 `0.2604` vs RepoNeedle `0.1896` ($-27.2\%$ deficit) - - Root Cause: In informal issue comments, exact lexical term matching is the strongest signal. RepoNeedle's workflow router dilutes BM25 weights by allocating probability mass to semantic and graph components, which introduces noise when AST symbols do not directly align with colloquial issue text. + - Root Cause *(corrected 2026-11-10; the earlier explanation attributed the deficit to "colloquial issue text" and router weight dilution, which the later evidence does not support)*: the queries are structured JSON PR-review contexts, not colloquial issue text. Much of the official-harness gap is a measurement artifact: RepoNeedle receives file fragments and unpacked files are scored in corpus order. On matched whole-file input, MRR is level with BM25 while Recall@20 remains significantly lower. Development-only lexical forensics point to query-term-frequency handling as the strongest candidate cause; no lexical change has been validated. See `KNOWN_LIMITATIONS.md` §4 and `reports/experimental/`. ### Claim 6: "Heuristic selective abstention reliably identifies no-gold tasks on ARB v2" - **Status**: **REFUTED / NOT SUPPORTED** @@ -110,8 +130,8 @@ This document provides a line-by-line verification of all scientific, empirical, - Counterfactual Wrong-Repo Queries ($N=32$): **0 / 32 correctly abstained (0.0% accuracy)**. - Combined Benchmark ($N=82$): **0 / 82 correctly abstained (0.0% accuracy)**. - Empirical Observations: - - Default abstention threshold: $\tau = 0.38$. - - Observed heuristic confidence range on no-gold queries: `[0.64, 0.86]`. + - Engine default abstention threshold: $\tau = 0.12$ (`BudgetConfig.abstention_threshold`). *Corrected 2026-11-09: earlier text stated 0.38, which is only the standalone `SelectiveAbstainer` class default and is not used by the engine.* + - Observed heuristic confidence on the 82 no-gold queries: range `[0.532, 0.891]`, 5th–95th percentile `[0.635, 0.851]`. *Corrected 2026-11-09: earlier text gave `[0.64, 0.86]`. Source: `reports/full_arb_validation/checkpoint_selective.jsonl`.* - Every single query exceeded the threshold, producing a 100% false return rate (zero abstention recall). - Diagnostic Explanation: Surface vocabulary overlap and score peakiness ($Z$-score) fail on full repositories because: 1. Real GitHub issues filed against a repository naturally contain identifiers that exist within the repository. @@ -160,7 +180,7 @@ This document provides a line-by-line verification of all scientific, empirical, ### What RepoNeedle DOES Achieve: 1. **Proven Superiority over Classical Lexical & BM25**: $+50.5\%$ relative gain on MRR ($p = 2.65 \times 10^{-6}$) and $+68.2\%$ relative gain on BCY@8k across 345 real-world repository samples. -2. **State-of-the-Art CPU Regression Test Discovery (`code2test`)**: $+151.7\%$ MRR over BM25 and $+37.0\%$ over RepoMap via inverted dependency graph AST linkages. +2. **Strongest Regression Test Discovery Among the Evaluated Baselines (`code2test`)**: $+151.7\%$ MRR over BM25 and $+37.0\%$ over RepoMap via inverted dependency graph AST linkages. 3. **High Context Yield Under Strict Token Budgets**: $+20.4\%$ higher BCY@4k than RepoMap, packing multi-tier structural skeletons to prevent context overflow. 4. **Sub-Second CPU Execution**: $142.4\text{ ms}$ median retrieval latency across real open-source codebases without requiring GPU infrastructure. 5. **Zero Data Contamination**: Strict isolation of proprietary or copyleft non-permissive datasets; 100% permissively licensed MIT release. @@ -168,7 +188,7 @@ This document provides a line-by-line verification of all scientific, empirical, ### What RepoNeedle DOES NOT Achieve: 1. **Conclusive Statistical Superiority Over RepoMap**: Overall MRR difference ($+0.0148$) is statistically insignificant ($p = 0.4089$), and RepoMap achieves substantially higher deep recall (Recall@20: $0.6306$ vs $0.4279$). 2. **Reliable Selective Abstention on Full Codebases**: Heuristic confidence gating achieved 0.0% accuracy on ARB v2 no-gold benchmarks; a trained neural discriminator is mandatory. -3. **Colloquial Issue Comment Dominance (`comment2context`)**: Classical BM25 achieves higher MRR ($0.1974$ vs $0.1708$) on conversational issue comments. +3. **Parity with BM25 on PR-review comment queries (`comment2context`)**: In the official harness, BM25 achieves higher MRR ($0.1974$ vs $0.1708$) on these structured JSON PR-review contexts. On matched whole-file input (supplemental, not official), MRR is level, but RepoNeedle's Recall@20 remains significantly lower. These are not conversational queries; ARB v2 does not measure conversational retrieval. 4. **Span-Level Retrieval**: RepoNeedle selects file-level units and structural skeletons, not fine-grained line-level spans. 5. **Deep Neural Foundation Model Representations**: RepoNeedle is a structured CPU ranking engine, not a pre-trained dense transformer embedding model. diff --git a/FINAL_RELEASE_AUDIT.md b/FINAL_RELEASE_AUDIT.md index 80a85a0..314f709 100644 --- a/FINAL_RELEASE_AUDIT.md +++ b/FINAL_RELEASE_AUDIT.md @@ -1,5 +1,8 @@ # FINAL RELEASE AUDIT: REPONEEDLE +> [!NOTE] +> **Historical record (2026-10-28) of the v0.1.0 research-preview release audit.** It has not been updated for later versions. For current status see [README.md](README.md), [CLAIMS_AND_EVIDENCE.md](CLAIMS_AND_EVIDENCE.md) and [KNOWN_LIMITATIONS.md](KNOWN_LIMITATIONS.md). + **Audit Date**: 2026-10-28 **Auditor**: Independent Senior Retrieval & Benchmark Auditor **Workspace**: `F:\RepoNeedle` diff --git a/HF_RELEASE_CHECKLIST.md b/HF_RELEASE_CHECKLIST.md index b205c53..f28febe 100644 --- a/HF_RELEASE_CHECKLIST.md +++ b/HF_RELEASE_CHECKLIST.md @@ -1,5 +1,8 @@ # HUGGING FACE & OPEN-SOURCE RELEASE CHECKLIST: REPONEEDLE +> [!NOTE] +> **Historical record (2026-10-28) of the v0.1.0 release checklist.** It has not been updated for later versions. The Hugging Face Space in `hf_artifacts/space/` bundles the 1.1.0 engine, in which the uncalibrated abstention heuristic is always on. + **Version**: `0.1.0` (Research Preview) **Date**: 2026-10-28 **Release Gate Verdict**: **APPROVED FOR LIMITED RESEARCH RELEASE** diff --git a/KNOWN_LIMITATIONS.md b/KNOWN_LIMITATIONS.md index 1060e3c..4e30949 100644 --- a/KNOWN_LIMITATIONS.md +++ b/KNOWN_LIMITATIONS.md @@ -1,8 +1,7 @@ # KNOWN LIMITATIONS AND ARCHITECTURAL BOUNDARIES: REPONEEDLE -**Version**: 0.1.0 Research Preview -**Status**: Active Research Pipeline -**Target Release**: Limited Research Release +**Applies to**: Released 1.1.0 (PyPI) and the current development branch (planned for 1.2.0, unreleased). Differences between the two are called out explicitly. +**Originally written for**: 0.1.0 Research Preview (October 2026); last reconciled with the source code on 2026-11-09. **Primary Benchmark**: Agent Retrieval Bench v2 ($N=427$ samples, 25 repositories, 271 unique snapshots) --- @@ -23,22 +22,49 @@ On the official Agent Retrieval Bench Selective Retrieval split ($N=82$ total ab - Counterfactual Wrong-Repo Tasks ($N=32$): **0 / 32 correctly abstained (100% false return rate)** ### Root Cause Analysis -The `SelectiveAbstainer` module uses a dual-signal heuristic: -1. **Z-Score Peakiness**: Measures whether top BM25 scores stand out significantly from the background distribution of document scores ($Z = \frac{s_1 - \mu}{\sigma}$). -2. **Repository Symbol Overlap**: Measures the proportion of query tokens matching extracted AST symbols or file paths. +The `SelectiveAbstainer` heuristic (`reponeedle/core/abstention.py`) computes a composite score over the engine's **fused ranking scores**: -In production benchmark conditions on complete repositories (often containing 500 to 5,000 files), this heuristic fundamentally breaks down: -1. **Natural Issues Contain Repository Vocabulary**: Natural issue descriptions filed against an open-source project inherently use terms relevant to that project (e.g. class names, module references, feature names). Even when the issue describes an external dependency bug, the text contains valid repo vocabulary, driving the overlap score above `0.70`. -2. **Counterfactual Queries Share Ubiquitous Software Primitives**: Counterfactual tasks taken from other repositories still use universal software engineering terms (`client`, `server`, `parse`, `config`, `handler`, `error`, `connect`). In any substantial repository, these terms match numerous valid functions, causing $Z$-scores to register between `2.5` and `4.0`. -3. **Threshold Mismatch**: The calibrated threshold of $\tau = 0.38$ was calibrated on small synthetic toy corpora. On full-scale repositories, heuristic confidence scores consistently fell in the range `[0.64, 0.86]`, never falling below $\tau$. +$$\text{confidence} = 0.30 \cdot \min(1, Z/3) + 0.45 \cdot \text{overlap} + 0.25 \cdot \text{margin}$$ + +1. **Repository Vocabulary Overlap**: fraction of query tokens (length > 2) found in the repository vocabulary (paths, symbols, and identifiers from the first 3,000 characters of each file). +2. **Z-Score Peakiness**: $(s_{\max} - \mu) / \sigma$ of the fused scores. +3. **Score Margin**: top fused score minus the median fused score. + +It abstains if overlap ≤ 0.10, or if confidence falls below the threshold. The engine's default threshold is **$\tau = 0.12$** (`BudgetConfig.abstention_threshold`, unchanged since the first release). The value 0.38 is only the standalone `SelectiveAbstainer` class default and is not used by the engine. + +Measured on the 82 ARB v2 no-gold queries (source: `reports/full_arb_validation/checkpoint_selective.jsonl`): +1. **Natural Issues Contain Repository Vocabulary**: Issue descriptions filed against a project naturally use that project's identifiers. Overlap had a median of 0.84 (range 0.39–0.92) on natural no-gold issues; 43 of 50 exceeded 0.70. +2. **Counterfactual Queries Share Ubiquitous Software Terms**: Queries from other repositories still use universal terms (`client`, `parse`, `config`, `handler`, `error`), which match many files in a large repository. Overlap had a median of 0.81 (range 0.61–0.92); the $Z$-score had a median of 4.32 (range 1.77–7.50). +3. **Threshold Far Below Observed Scores**: Confidence on all 82 no-gold queries ranged from **0.532 to 0.891** (5th–95th percentile 0.635–0.851), so no query came near $\tau = 0.12$. Raising the threshold would not fix this without also rejecting in-scope queries, because the heuristic does not separate the two populations; it would need to be evaluated against the false-abstention rate on positive queries. ### Required Resolution Heuristic confidence scoring based on surface lexical overlap is scientifically inadequate for repository abstention. Future versions will require a dedicated, fine-tuned cross-encoder or neural classifier trained specifically to discriminate internal bugs from out-of-scope issues. Until then, RepoNeedle's selective abstention feature must be treated as purely experimental. +### Mitigation on the Development Branch (planned 1.2.0): Disabled by Default (Behavior Change) +In released 1.1.0 the heuristic runs on every query. On the development branch it is no longer applied unless explicitly requested. The algorithm, confidence formula and threshold semantics are unchanged; only the default changed. + +| Surface | How to opt in | +| :--- | :--- | +| Python API (engine-wide) | `BudgetConfig(enable_experimental_abstention=True)` | +| Python API (per call) | `engine.retrieve(..., enable_experimental_abstention=True)` (also accepts `False` to override an engine-wide opt-in) | +| CLI | `--enable-experimental-abstention` | +| MCP | `reponeedle_retrieve` argument `enable_experimental_abstention: true` | + +Consequences users must understand: +- **Out-of-scope queries are not detected by default.** Retrieval returns the best-ranked files even when the repository does not contain the answer. Treat results as candidates, not as evidence that the answer exists in the repository. +- **`task_type="selective"` does not enable abstention.** It only selects routing weights. This prevents the opt-in requirement from being bypassed by a task label. +- **`confidence_score` is uncalibrated.** It is still computed on every query for schema compatibility, but it is not a probability and should not be used as an in-scope / out-of-scope classifier. +- **Empty repositories still abstain** (`abstention.status == "empty_repository"`) because there are no candidates; this is structural, not heuristic. +- Every `RetrievalResult` carries an `abstention` diagnostic object with `experimental: true` and `calibrated: false`, so downstream consumers cannot mistake the heuristic for a calibrated signal. +- Every benchmark or research script that measures the heuristic opts in explicitly, so historical abstention results remain reproducible; ordinary retrieval benchmarks use the new default. The per-script policy, and which historical results were measured under the old always-on default, are documented in [docs/BENCHMARK_CONFIGURATION.md](docs/BENCHMARK_CONFIGURATION.md) and enforced by `tests/test_documentation_consistency.py`. + --- ## 3. Deep Recall Deficit Compared to RepoMap +> [!NOTE] +> Figures in this section come from the 2026-10-28 engine (README Part A). On the current engine (1.1.0 / development branch, re-measured 2026-11-09) the gap is larger: Recall@20 is 0.3842 vs RepoMap 0.6306 (RepoMap +64.2%), and BCY@8k is 0.3443 vs 0.3788 (RepoMap +10.0%). MRR remains at parity (+0.0013, repository-clustered CI [−0.0597, +0.0517]). See [docs/BENCHMARK_CONFIGURATION.md §7.3](docs/BENCHMARK_CONFIGURATION.md#73-current-engine-re-measurement-arb-v2-positive-n345). + ### The Observed Defect While RepoNeedle matches or slightly exceeds RepoMap on top-rank precision (MRR: `0.2287` vs `0.2140`, a non-significant +6.9% difference, $p=0.4089$), **RepoMap substantially outperforms RepoNeedle at high recall depth**: - **Recall@20**: RepoMap `0.6306` vs RepoNeedle `0.4279` (**+47.4% relative advantage for RepoMap**) @@ -55,6 +81,15 @@ While RepoNeedle matches or slightly exceeds RepoMap on top-rank precision (MRR: ## 4. Performance Deficit on High-Level Issue Comments (`comment2context`) +> [!NOTE] +> Figures in this section come from the 2026-10-28 engine. On the current engine (re-measured 2026-11-09) the deficit is slightly larger: `comment2context` MRR 0.1658 vs BM25 0.1974 (**−16.0%**), Recall@20 0.1938 vs 0.5292. +> +> A supplemental product-aligned evaluation (whole-file input for both systems, file-level full ranking; not an official ARB score) shows that much of this official-harness gap is a measurement artifact. On matched whole-file input, RepoNeedle is level with BM25 on `comment2context` MRR (−0.0026, CI includes zero). A **genuine deep-recall deficit remains**, however: Recall@20 0.4896 vs 0.5792 (−0.0896, repository-clustered 95% CI [−0.1779, −0.0056]). See `reports/experimental/phase1_product_aligned_evaluation/REPORT.md`. +> +> The existing opt-in documentation filter (`enable_doc_filter`) was tested as a mitigation in a pre-registered held-out experiment and was **not validated**: it left comment2context Recall@20 unchanged on held-out repositories (Δ 0.000), though it removed documentation from top ranks and showed no regressions. It also demoted 1 of 4 gold documentation files in development data. It remains off by default. See `reports/experimental/phase2_documentation_filter/REPORT.md`. +> +> Lexical forensics (development data only) attribute most of RepoNeedle's comment2context lexical gap to its BM25 index **ignoring query term frequency**, plus duplicate token counting. Accounting for query term frequency also harms trace2code on development data, so no change has been made. See `reports/experimental/phase3_lexical_retrieval/REPORT.md`. + ### The Observed Defect On the `comment2context` task track ($N=80$ samples), classical BM25 outperforms RepoNeedle: - **MRR**: Official BM25 `0.1974` vs RepoNeedle `0.1708` (**-13.5% deficit for RepoNeedle**) @@ -63,9 +98,13 @@ On the `comment2context` task track ($N=80$ samples), classical BM25 outperforms - **BCY@8k**: Official BM25 `0.2604` vs RepoNeedle `0.1896` (**-27.2% deficit**) ### Root Cause -1. **Colloquial Text vs AST Symbols**: Issue comments are written in conversational prose (e.g. "could we make timeouts configurable in the client?"). They often lack exact class or function names. -2. **Weight Dilution**: `WorkflowRouter` classifies `comment2context` queries and distributes score weights across BM25 (0.50), Semantic (0.35), and Graph (0.15). In the absence of dense neural embeddings, the subword character $n$-gram semantic index can introduce noise, diluting the sharp term-frequency signal that pure BM25 provides. -3. **Mitigation**: In applications where queries are predominantly high-level human discussion, BM25 weight should be manually set to $\ge 0.85$. +*(Corrected 2026-11-10. Earlier text described these queries as conversational prose, quoted fusion weights the code does not use, and recommended a BM25 weight of at least 0.85; none of that matched the code or the evidence.)* + +1. **Query form**: ARB v2 `comment2context` queries are **structured JSON PR-review contexts** (`review_comment`, `diff_hunk_context`, `path`, `given_file`, `pr_title`, `line`), not conversational natural-language questions. ARB v2 has no conversational query track, so conversational retrieval is not measured by these results. +2. **Fusion weights (verified in `reponeedle/core/router.py`)**: for `comment2context` the router uses lexical 0.40, semantic 0.40, structural 0.20, workflow boost 0.0. The structural term multiplies a PageRank score that is all zeros unless `enable_pagerank_fusion=True` (default `False`), so in the default configuration and in the audited benchmarks the graph contributes nothing. +3. **Measurement effects**: much of the official-harness gap is an artifact of how the harness feeds RepoNeedle file fragments and scores unpacked files in corpus order (Phase 0 / Phase 1 reports). On matched whole-file input, `comment2context` MRR is level with BM25, but a real deep-recall deficit remains (Recall@20 −0.0896, repository-clustered 95% CI [−0.1779, −0.0056]). +4. **Lexical findings (research, not a production fix)**: development-only forensics (Phase 3) identified **query-term-frequency handling** as the strongest candidate cause of the lexical gap: RepoNeedle's BM25 index counts query terms but does not use the counts. Accounting for raw query term frequency improved `comment2context` lexical Recall@20 on development data but caused a `trace2code` regression. The best combination of the remaining candidate changes did not meet the pre-registered development criterion. **No lexical optimisation was validated, and production lexical behaviour is unchanged.** +5. **No supported mitigation yet**: raising the BM25 weight is **not** recommended. Lexical-only ranking performed worse than the current fusion on both fragment and whole-file input (Phase 0, Phase 3). The opt-in documentation filter was also not validated (Phase 2). --- @@ -79,6 +118,27 @@ In repositories containing large monolithic source files (>2,000 lines of code, --- +## 5a. Indexing Exclusions: File-Size Limit, Unreadable Files and `max_files` + +### Behavior +`RepoNeedleEngine.from_directory` does not index every file in a repository: +1. **File-size limit**: files **strictly larger** than `max_file_size_bytes` (default `500_000` bytes) are excluded. A file exactly at the limit is indexed. The limit is configurable (`from_directory(..., max_file_size_bytes=...)`, CLI/MCP `--max-file-size-bytes`) and must be a positive integer. It is never raised automatically. +2. **Unreadable files**: files whose size or contents cannot be read are excluded (including broken links and link loops). +2b. **Links resolving outside the repository** (development branch): files whose canonical location (following symlinks, and junctions on Windows) is outside the repository root are excluded without reading the target and recorded by link path (`symlink_outside_repository_paths`). In released 1.1.0 such files were read and indexed. +3. **`max_files` cap**: indexing stops after `max_files` (default `3000`) files. +4. **Ignore rules and extensions**: `.gitignore` entries, built-in ignore patterns (`node_modules`, `dist`, `build`, …) and unsupported extensions are excluded by design and are **not** reported as skipped. + +### Visibility (development branch, planned 1.2.0) +Exclusions in categories 1–3 are recorded in `engine.indexing_diagnostics` (`skipped_paths`, `unreadable_paths`, `max_files_reached`, plus counts), exposed in CLI `--json` output under `indexing`, and returned by the MCP `reponeedle_inspect` tool. A summary `WARNING` is emitted on the `reponeedle.engine` logger (stderr by default; never on JSON stdout). Diagnostics and log records contain repository-relative paths and counts only, never file contents. + +### Remaining Limitations +- Excluded files are invisible to retrieval: a relevant symbol defined only in an oversized generated or vendored file cannot be found. +- `skipped_paths` is unbounded. A repository with thousands of oversized supported-extension files (e.g. large JSON fixtures) produces a correspondingly long list in JSON/MCP output. +- Which files are dropped by the `max_files` cap depends on filesystem walk order, which is not guaranteed to be stable across platforms. +- The MCP server indexes once at startup; diagnostics reflect that snapshot until the server is restarted. + +--- + ## 6. Local Compute Boundaries & Neural Baseline Scope ### Execution Boundaries @@ -105,10 +165,33 @@ To ensure that headline gains over BM25 (+50.5%) were not simply driven by large | Limitation | Impact | Severity | Planned Mitigation | | :--- | :--- | :---: | :--- | -| **Heuristic Abstention Failure** | 0.0% accuracy on no-gold tasks | **HIGH** | Fine-tuned neural cross-encoder discriminator | +| **Heuristic Abstention Failure** | 0.0% accuracy on no-gold tasks; always on in 1.1.0, disabled by default on the development branch (planned 1.2.0), where out-of-scope queries are therefore not detected | **HIGH** | Fine-tuned neural cross-encoder discriminator | +| **Indexing Exclusions** | Files > 500 KB (configurable via `max_file_size_bytes` on the development branch), unreadable files, or beyond `max_files` are not searchable. Silent in 1.1.0; reported in diagnostics on the development branch | **MEDIUM** | Configurable limit; future span-level indexing of large files | | **Deep Tail Recall Deficit** | Recall@20 lags RepoMap by -32.1% | **MEDIUM** | Multi-hop graph diffusion for tail ranks | | **`comment2context` Deficit** | MRR lags pure BM25 by -13.5% | **MEDIUM** | Dynamic BM25 up-weighting on colloquial inputs | | **Monolithic File Saturation** | 2k+ line files crowd budget | **MEDIUM** | AST block/span-level context slicing | | **CPU Only / No Neural Weights** | Cannot perform deep semantic reasoning | **LOW (Design Choice)** | Optional pluggable neural embedding backend | | **Benchmark Cluster Imbalance** | Top 3 repos represent 48% of queries | **LOW** | Expanded evaluation on Multi-Repo Benchmarks | +--- + +## 9. Capabilities Not Implemented + +These are frequently assumed but are **not** part of RepoNeedle (neither 1.1.0 nor the current development branch): + +| Capability | Status | +| :--- | :--- | +| Persistent / on-disk index cache | Not implemented; the index is rebuilt in memory on every process start. | +| Automatic live filesystem updates | Not implemented; restart the CLI process or MCP server to pick up changes. | +| HTTP / SSE MCP transport | Not implemented; the MCP server supports STDIO only. | +| AST block slicing of large files | Not implemented (see Section 5). | +| Calibrated confidence / reliable abstention | Not implemented; `confidence_score` is an uncalibrated diagnostic heuristic (see Section 2). | +| Live validation in Claude Code / Cursor | Not performed; MCP is verified with the MCP Python SDK and a JSON-RPC STDIO handshake only. | + +## 10. Benchmark Reproducibility & Research Integrity Notes + +Several historical artifacts cannot be reproduced exactly or contain non-measured values. They are documented, not silently corrected, in [docs/BENCHMARK_CONFIGURATION.md](docs/BENCHMARK_CONFIGURATION.md#research-integrity-notes). In summary: +- `benchmarks/results/benchmark_results.json` (synthetic, v0.1.0) is not reproduced by 1.1.0 or later code. +- Stage 3.6 per-query RepoMap BCY@4k/BCY@16k values and latencies were derived or hard-coded, not measured. This does **not** affect the headline ARB v2 results in the README, which use the official `pack_files` measurement for every system. +- The Stage 3.6 abstention evaluation simulated positive-query confidence scores instead of measuring them. + diff --git a/README.md b/README.md index 3853084..d32e6c7 100644 --- a/README.md +++ b/README.md @@ -6,11 +6,96 @@ [![Hugging Face Dataset](https://img.shields.io/badge/%F0%9F%A4%97%20Dataset-RepoNeedle--Bench-blue)](https://huggingface.co/datasets/RepoNeedle/RepoNeedle-Bench) [![Hugging Face Space](https://img.shields.io/badge/%F0%9F%A4%97%20Space-RepoNeedle--Playground-indigo)](https://huggingface.co/spaces/RepoNeedle/RepoNeedle-Playground) -**RepoNeedle** is an open-source, token-budget-aware repository context retrieval system designed to help AI coding agents discover the most critical files for a coding task under strict context-token constraints. +**RepoNeedle finds the files an AI coding agent needs for a task, and packs them into a token budget you choose.** + +Give it a bug report, an error traceback, or a request like "find the tests for `SessionManager`", and it returns a ranked selection of files, focused code spans and structural outlines that fits inside the context window you specify. It runs locally on your CPU, with no GPU, API key, model download or network access. + +--- + +## 💡 Why RepoNeedle? + +- **Agents cannot read a whole repository.** Coding agents (Claude Code, Cursor, Aider, and others) have to decide which files to load into a limited context window. Loading too much wastes tokens and money and buries the relevant code; loading too little misses it. +- **File size matters as much as relevance.** A search that ignores file size can spend an entire 4,000-token budget on one large file. RepoNeedle scores files by relevance *per token* and falls back to focused spans or signatures-only outlines when a full file does not fit. It never exceeds the budget, as measured by its own token model. +- **Different tasks need different files.** A traceback points at specific frames, a "write tests" request needs test files, and a refactor needs dependent modules. RepoNeedle adapts its ranking to the kind of task. +- **CPU-first and local.** It is plain Python (NumPy, SciPy, NetworkX), so it runs on laptops and CI machines, keeps your code on your machine, and needs no model weights. + +RepoNeedle is a research-oriented project: its benchmark results, including where it does **not** beat simpler baselines, are documented in detail [below](#-benchmark-validation) and in [KNOWN_LIMITATIONS.md](KNOWN_LIMITATIONS.md). + +--- + +## 📌 Release Status + +| Feature | 1.1.0 (released, PyPI) | Development branch (planned 1.2.0, **unreleased**) | +| :--- | :---: | :---: | +| CPU-first execution | ✅ | ✅ | +| Python 3.10, 3.11, 3.12 | ✅ | ✅ | +| Python API with five rankers (`legacy`, `bm25`, `arm101`, `cascade`, `change_impact`) | ✅ | ✅ | +| CLI with Rich output (`--ranker`, `--task`, `--mode`, `--output-file`) | ✅ | ✅ | +| CLI JSON output (`--json`) | — | ✅ | +| CLI anchor files (`--anchors`) | — | ✅ | +| MCP server over STDIO (`reponeedle-mcp`) | — | ✅ (protocol-tested; not yet validated in a live Claude Code / Cursor session) | +| Selective abstention heuristic (experimental, uncalibrated) | Always on | **Disabled by default**, opt-in | +| Indexing diagnostics & configurable file-size limit | — (fixed 500 KB limit, skipped files not reported) | ✅ | +| Persistent index cache | Not implemented | Not implemented | +| Automatic live filesystem updates | Not implemented | Not implemented | +| HTTP / SSE MCP transport | Not implemented | Not implemented | +| AST block slicing of large files | Not implemented | Not implemented | + +Features marked for the development branch require installing from source (see below) until 1.2.0 is published. + +--- + +## 🚀 Quickstart + +### 1. Install + +```bash +# Released version (1.1.0) from PyPI +pip install reponeedle + +# Development branch: adds --json, --anchors, the MCP server and indexing diagnostics +git clone https://github.com/krishika08/RepoNeedle.git +cd RepoNeedle +pip install -e . # core +pip install -e ".[mcp]" # core + MCP server +``` + +### 2. Try the CLI + +```bash +# Rank and pack context for a task (works with 1.1.0 and the development branch) +reponeedle "where is the session cookie saved?" --repo ./my-project --budget 4000 + +# Development branch: machine-readable JSON for scripts and agents +reponeedle "fix token expiration in auth" --repo ./my-project --json +``` + +### 3. Use it from Python + +```python +from reponeedle import RepoNeedleEngine + +engine = RepoNeedleEngine.from_directory("./my-project") +result = engine.retrieve("Fix the session timeout bug", budget_tokens=4000) + +for f in result.selected_files: + print(f.path, f.tier.value, f.token_count) + +prompt_context = result.rendered_context # ready-to-paste markdown for an LLM +``` + +### 4. Connect an MCP client (development branch) + +```bash +pip install -e ".[mcp]" +claude mcp add reponeedle -- python -m reponeedle.mcp --repo /absolute/path/to/your/project +``` + +The server exposes `reponeedle_retrieve`, `reponeedle_change_impact` and `reponeedle_inspect`. See the [MCP Integration Guide](docs/MCP_INTEGRATION.md) for Cursor and other clients. The server is tested at the protocol level (MCP Python SDK and a JSON-RPC STDIO handshake); it has not yet been validated in a live Claude Code or Cursor session. --- -## ⚡ The Upstream Context Problem in AI Coding +## 🧩 How It Works (Technical Overview) Autonomous coding agents (e.g., Claude Code, Aider, OpenHands, Cline) face a severe upstream bottleneck: **Context Acquisition**. @@ -24,16 +109,228 @@ RepoNeedle solves this upstream bottleneck by combining: 1. **Multi-Signal Hybrid Scoring**: Field-weighted BM25 + Subword Semantic Vectors + AST Repository Graph Centrality. 2. **Workflow-Adaptive Routing**: Automatic classification of query intent (`trace2code`, `code2test`, `edit2ripple`, `comment2context`, `selective`) with dynamic weight tuning. 3. **Inverted Test-to-Target Mapping**: Dependency graph inversion and naming convention heuristics to locate regression test suites. -4. **Calibrated Selective Abstention**: Peak-to-noise ($Z$-score) and repository symbol overlap gating to abstain from returning noise on counterfactual tasks. +4. **Experimental Selective Abstention (opt-in, disabled by default)**: Peak-to-noise ($Z$-score), score-margin and repository symbol overlap heuristic that can abstain on out-of-scope queries. It is **not production-calibrated** (0/82 correct abstentions on the ARB v2 selective split; see Part B) and must be explicitly enabled. 5. **CUD-Pack (Capacity-Sensitive Utility-Density Packing)**: Adaptive knapsack selection engine providing strict zero downstream overflows ($C_{\text{actual}} \le B_{\text{configured}}$) according to RepoNeedle's regex code token model across dual modes: **Compact Mode** ($\le 4\text{k}$ tokens, extracting high-density implementation spans and structural skeletons) and **Complete Context Mode** ($\ge 8\text{k}$ tokens, preserving whole source files). 6. **Change-Impact Ranking Engine (V1.1 Opt-in)**: Seed-relative relationship scoring (evaluating static imports, symbol definitions, call sites, build manifests (`setup.py`, `setup.cfg`, `pyproject.toml`, `cargo.toml`, `package.json`, `go.mod`, `pom.xml`), test-stems, and directory proximity) designed for bug fixing and refactoring tasks. --- +## 📖 Usage Guide + +RepoNeedle is self-contained and requires standard Python dependencies (`numpy`, `scipy`, `networkx`, `pydantic`, `pathspec`, `rich`, and `tomli` on Python < 3.11). + +### 1. Installation Details + +| What you need | Command | +| :--- | :--- | +| Released 1.1.0 (Python API, Rich CLI) | `pip install reponeedle` | +| Development branch (adds `--json`, `--anchors`, indexing diagnostics) | `pip install -e .` (from a clone) | +| Development branch + MCP server | `pip install -e ".[mcp]"` | +| Test and development tools | `pip install -e ".[dev]"` | + +> [!NOTE] +> `pip install "reponeedle[mcp]"` from PyPI currently installs 1.1.0, which does not include the MCP server. Install from source for MCP support until 1.2.0 is published. + +> [!TIP] +> On Python 3.10, `tomli>=2.0.1` is automatically installed via conditional dependency. On Python 3.11 and 3.12, native standard library `tomllib` is used with zero extra dependencies. + +--- + +### 2. Python API + +#### Standard Retrieval (Default Legacy Ranker): +```python +from reponeedle import RepoNeedleEngine, BudgetConfig + +# 1. Initialize engine on a local codebase (uses default 'legacy' ranker) +engine = RepoNeedleEngine.from_directory( + repo_dir="./my-project", + budget_config=BudgetConfig(max_tokens=4000, allow_skeletons=True, mode="auto"), +) + +# 2. Retrieve context for a task (auto-resolves to compact mode for <= 4k) +result = engine.retrieve( + query="Add support for blueprint url_prefix inheritance in route registration", + budget_tokens=4000, +) + +if result.abstained: + print(f"Abstained: {result.explanation}") +else: + print(f"Retrieved {len(result.selected_files)} files ({result.total_tokens} / {result.budget_tokens} tokens):") + for f in result.selected_files: + print(f"[{f.tier.value.upper()}] {f.path} (score: {f.score:.3f}, tokens: {f.token_count})") + + # Access the exact fenced-markdown serialized prompt ready for LLM consumption: + llm_prompt_context = result.rendered_context +``` + +> [!NOTE] +> With default settings `result.abstained` is `True` only for an empty repository (no candidate files). Heuristic out-of-scope abstention is opt-in; see below. + +#### Experimental Abstention (Opt-In) + +> [!WARNING] +> **Behavior change on the development branch (planned 1.2.0, unreleased)**: the selective abstention heuristic is **disabled by default**. In released 1.1.x it runs on every query. Because it is not production-calibrated (0 of 82 negative samples correctly abstained on the ARB v2 selective split), leaving it on gave a misleading impression that out-of-scope queries were being filtered. With it disabled, retrieval always returns ranked results (except for an empty repository) and **out-of-scope queries are not detected**. If you relied on the 1.1.x behavior, opt in explicitly as shown below. + +```python +from reponeedle import RepoNeedleEngine, BudgetConfig, TaskType + +# Engine-wide opt-in (abstention_threshold keeps working as before) +engine = RepoNeedleEngine.from_directory( + repo_dir="./my-project", + budget_config=BudgetConfig(enable_experimental_abstention=True, abstention_threshold=0.12), +) + +# ...or per call, overriding the engine setting in either direction +result = engine.retrieve("Cargo.toml tokio runtime leak", enable_experimental_abstention=True) + +print(result.abstained) # decided by the heuristic only because we opted in +print(result.abstention.status) # "disabled" | "evaluated" | "empty_repository" +print(result.abstention.experimental) # always True +print(result.abstention.calibrated) # always False +``` + +- **`TaskType.SELECTIVE` does not enable abstention.** Selecting the `selective` task only changes routing weights; the opt-in flag is the only switch. +- `confidence_score` is still computed with the unchanged heuristic formula for every result. It is an **uncalibrated signal**, not a probability that the answer is in the repository. +- Empty repositories always return `abstained=True` (`abstention.status == "empty_repository"`), independent of the flag, because there is nothing to return. + +#### Confidence Scores Are Diagnostic, Not Probabilities + +`RetrievalResult.confidence_score` (also reported by the CLI `--json` output and the MCP `reponeedle_retrieve` tool) is the raw output of the experimental abstention heuristic: a weighted mix of vocabulary overlap, score peakiness and score margin. + +- It is **not calibrated** and is **not** a probability that the repository contains the answer. On the ARB v2 no-gold queries it was 0.53–0.89, overlapping the values seen on in-scope queries. +- It is computed on every query, whether or not abstention is enabled, so its value does not depend on the opt-in flag. +- With abstention disabled (the development-branch default), it **never** prevents normal retrieval. +- Whether the heuristic was applied is reported explicitly in `result.abstention` (`enabled`, `status`, `experimental: true`, `calibrated: false`) in the Python API, in the `abstention` object of CLI `--json` output, and in the MCP retrieve response. + +Do not use `confidence_score` to decide whether an answer exists in the repository. + +#### Indexing Limits & Diagnostics + +`from_directory` skips files **strictly larger** than `max_file_size_bytes` (default `500_000`; a file exactly at the limit is indexed). Skipped files are never silently dropped: they are recorded in `engine.indexing_diagnostics` and summarized by a `WARNING` on the `reponeedle.engine` logger (stderr by default). Diagnostics contain repository-relative paths and counts only, never file contents. + +```python +from reponeedle import RepoNeedleEngine + +engine = RepoNeedleEngine.from_directory("./my-project", max_file_size_bytes=1_000_000) + +diag = engine.indexing_diagnostics +print(diag.indexed_files, diag.skipped_large_files, diag.max_file_size_bytes) +print(diag.skipped_paths) # e.g. ["generated/large_module.py", "vendor/huge_source.cpp"] +print(diag.unreadable_paths) # files that could not be read +print(diag.symlink_outside_repository_paths) # links resolving outside the repo (target never read) +print(diag.max_files_reached) # True if indexing stopped at max_files (default 3000) +``` + +`max_file_size_bytes` must be a positive integer: `0` and negative values raise `ValueError`, non-integers raise `TypeError`. The default is unchanged and is never raised automatically. Only files with a supported extension and not excluded by ignore rules (`.gitignore` and the built-in patterns) are counted. + +#### Opt-In: Seed-Based Change-Impact Retrieval: +```python +from reponeedle import RepoNeedleEngine, Query + +# Initialize engine with Change-Impact ranking +engine = RepoNeedleEngine.from_directory( + repo_dir="./my-project", + ranker="change_impact", +) + +# Populate anchor_files with the seed path to activate relationship scoring +query = Query( + text="Fix authentication session expiration", + anchor_files=["auth/session.py"], + budget_tokens=4000, +) + +result = engine.retrieve(query) + +for f in result.selected_files: + print(f"[{f.tier.value.upper()}] {f.path} (score: {f.score:.3f}, tokens: {f.token_count})") + if f.explanation: + print(f" Attribution: {f.explanation}") +``` + +> [!NOTE] +> **Behavior of Plain-String (Seedless) Change-Impact Queries**: +> The Change-Impact ranking engine reads seed file paths from `query.anchor_files`. When `engine.retrieve()` is called with a plain string query (or without `anchor_files`): +> - **Lexical and semantic scoring remain active**: Candidate files continue to be retrieved and scored using combined BM25 lexical matching and subword semantic retrieval. +> - **Seed suppression is inactive without `anchor_files`**: Seed File Suppression (SFS) is not triggered since there are no designated seed files to down-weight. +> - **Seed-relative relationship boosts are inactive without `anchor_files`**: Relationship scoring across dependencies (static imports, symbols, calls, build manifests, test-stems, and directory proximity) is skipped. +> - **Test Intent Prior adjustments remain available according to detected query intent**: Test files receive boosts or penalties (`tip_multiplier_test` / `tip_multiplier_non_test`) based on detected test intent in the query text. +> +> To activate seed-relative relationship boosts and seed suppression, pass seed paths via `Query(text=..., anchor_files=['path/to/seed.py'])`. + +--- + +### 3. Command Line Interface (CLI) + +```bash +# Default 'legacy' retrieval under a 4000 token limit +reponeedle "Fix AttributeError in SecureCookieSessionInterface cookie saving" --repo ./flask --budget 4000 + +# Seed-based Change-Impact retrieval with anchor files +reponeedle "Assess session timeout refactor" --repo ./my-project --ranker change_impact --anchors auth/session.py --budget 4000 + +# Machine-readable JSON output for AI agent pipelines +reponeedle "Fix authentication expiration" --repo ./my-project --json + +# Task-conditioned retrieval for finding regression tests +reponeedle "test suite for HTTPBasicAuth" --repo ./requests --task code2test --budget 2000 + +# Serialize packaged markdown directly to a file +reponeedle "blueprints and route registration" --repo ./flask --budget 4000 --output-file prompt.md + +# Opt in to the EXPERIMENTAL (uncalibrated) abstention heuristic +reponeedle "tokio runtime leak" --repo ./flask --json --enable-experimental-abstention + +# Raise the per-file size limit (default 500000 bytes); skipped files are reported +reponeedle "parser entry point" --repo ./my-project --json --max-file-size-bytes 1000000 +``` + +- `--task selective` does **not** enable abstention; only `--enable-experimental-abstention` does. +- With `--json`, stdout is strictly one JSON document. It contains an `abstention` object (`enabled`, `status`, `experimental`, `calibrated`, `threshold`, `reason`) and an `indexing` object (`indexed_files`, `skipped_large_files`, `skipped_paths`, `max_file_size_bytes`, `unreadable_paths`, …). Warnings about skipped files go to **stderr** only. +- Without `--json`, a yellow "Indexing notice" listing skipped paths is printed to **stderr**. +- An invalid `--max-file-size-bytes` (zero or negative) exits with code `1` and an error on stderr. + +--- + +### 4. Model Context Protocol (MCP) Server + +*Development branch only (planned 1.2.0; install with `pip install -e ".[mcp]"`).* RepoNeedle includes a local **MCP server** that lets MCP-capable coding agents call it over STDIO. It is tested with the MCP Python SDK and a JSON-RPC STDIO handshake; it has not yet been validated in a live Claude Code, Cursor, Cline or Windsurf session. + +```bash +# Launch the MCP server pointing to your project +reponeedle-mcp --repo /absolute/path/to/project --budget 4000 + +# Or via Python module +python -m reponeedle.mcp --repo /absolute/path/to/project +``` + +#### Supported MCP Tools: +- `reponeedle_retrieve`: Retrieves ranked source files, focus spans, and structural skeletons packed strictly within the token budget. +- `reponeedle_change_impact`: Analyzes modified anchor files to discover downstream affected modules and regression test suites using Seed File Suppression (SFS). +- `reponeedle_inspect`: Returns repository metadata, indexed file counts, sample paths, active engine configuration, **indexing diagnostics** (files skipped for exceeding `--max-file-size-bytes` or being unreadable) and the experimental abstention status, without dumping file contents. + +`reponeedle_retrieve` accepts an optional `enable_experimental_abstention` boolean (default `false`). The server's file-size limit is set at launch with `--max-file-size-bytes`. + +For client configuration examples (Claude Code, Cursor), see the [MCP Integration Guide](docs/MCP_INTEGRATION.md). + +--- + ## 📊 Benchmark Validation To maintain strict scientific integrity, **Official External Benchmark Results** are reported separately from **Independent Research Evaluations**. Dataset sizes, query counts ($N$), candidate pool properties, and evaluation methodologies are explicitly documented for every measurement. +> [!IMPORTANT] +> **Which engine these numbers describe.** Parts A–C and the latency table in Part D come from the ARB v2 run of 2026-10-28, made with the engine at commit `8e00425` (tag `v0.1.0-baseline`); that code reproduced the published per-query records exactly on every query checked (all 82 selective and 53 of 345 positive queries). Engine changes made before the 1.1.0 release altered RepoNeedle's rankings, so these results do **not** describe released 1.1.0. The current engine (1.1.0, identical to the development branch) was re-measured on the same ARB v2 positive split on 2026-11-09: +> +> | ARB v2 positive (N=345) | MRR | Recall@5 | Recall@10 | Recall@20 | BCY@4k | BCY@8k | +> | :--- | :---: | :---: | :---: | :---: | :---: | :---: | +> | **RepoNeedle, current engine (1.1.0)** | **0.2153** | **0.3032** | **0.3677** | **0.3842** | **0.2400** | **0.3443** | +> | RepoNeedle, 2026-10-28 engine (Part A) | 0.2287 | 0.3011 | 0.4110 | 0.4279 | 0.2436 | 0.3450 | +> +> The qualitative conclusions below still hold for the current engine: significantly higher MRR than BM25 (+0.0633, repository-clustered 95% CI [+0.0111, +0.0980]) and Lexical, parity with RepoMap on MRR, much lower deep recall than RepoMap, the strongest `code2test` MRR, and a `comment2context` deficit against BM25 (−16.0%). The current engine is somewhat weaker than the 2026-10-28 engine (MRR −0.0134, CI excludes zero). Full tables, per-task results and method: [docs/BENCHMARK_CONFIGURATION.md](docs/BENCHMARK_CONFIGURATION.md#73-current-engine-re-measurement-arb-v2-positive-n345). + --- ### Part A: Full Official Agent Retrieval Bench (ARB v2) Evaluation ($N=345$ Positive Samples) @@ -64,6 +361,9 @@ Evaluated with **10,000 bootstrap resamples** (repository-clustered bootstrap ac > [!IMPORTANT] > **Scientific Disclosure on RepoMap Comparison**: RepoNeedle does **NOT** achieve statistically significant overall superiority over RepoMap on MRR ($p = 0.4089$, repository-clustered 95% CI crosses zero: `[-0.0466, +0.0619]`). Furthermore, **RepoMap achieves substantially higher deep recall** (Recall@20: `0.6306` vs `0.4279`, a +47.4% relative advantage for RepoMap) and higher BCY@8k (`0.3788` vs `0.3450`, +9.8% relative advantage). RepoNeedle demonstrates a focused advantage under strict low-token budgets (BCY@4k: `0.2436` vs `0.2024`, +20.4% relative advantage) and specialized regression test discovery (`code2test`). +> [!NOTE] +> **Lexical comparison caveat**: The RepoNeedle vs Lexical difference is significant under the paired $t$-test and the repository-clustered bootstrap, but **not** under the Wilcoxon signed-rank test ($p = 0.1215$), and RepoNeedle wins fewer individual queries than it loses (133 / 29 / 183). The mean gain comes from a smaller number of large wins. + #### Per-Task Track Breakdown ($N=345$ Positive Samples) | Task Track | Sample Count ($N$) | Metric | Official BM25 | Official Lexical | Official RepoMap | RepoNeedle | Relative Gain vs BM25 | Relative Gain vs RepoMap | @@ -105,8 +405,9 @@ Evaluated on the complete official Agent Retrieval Bench Selective Retrieval spl > [!CAUTION] > **Heuristic Abstention Failure Analysis**: In production benchmark conditions, RepoNeedle's heuristic confidence gating ($Z$-score + repository symbol overlap) achieved **0% abstention accuracy (0/82)**. -> - *Root Cause*: Heuristic confidence scores ranged between `0.64` and `0.86`, far exceeding the default threshold (`0.12` / `0.38`). Natural GitHub issue descriptions filed against a repository inherently contain valid repository vocabulary. Counterfactual queries similarly contain ubiquitous software engineering terminology (`error`, `parse`, `client`, `handler`). +> - *Root Cause*: Heuristic confidence on the 82 no-gold queries ranged from `0.532` to `0.891` (5th–95th percentile `0.635`–`0.851`), far above the engine's default threshold of `0.12`. Natural GitHub issue descriptions filed against a repository inherently contain valid repository vocabulary. Counterfactual queries similarly contain ubiquitous software engineering terminology (`error`, `parse`, `client`, `handler`). > - *Recommendation*: Heuristic score thresholds are insufficient for reliable abstention in large codebases. Selective retrieval requires a dedicated, trained neural discriminator rather than surface lexical statistics. +> - *Consequence*: In released 1.1.0 the heuristic is always on. On the development branch (planned 1.2.0, unreleased) it is **disabled by default** and only applied when explicitly enabled. See [Experimental Abstention](#experimental-abstention-opt-in). --- @@ -156,7 +457,8 @@ Empirically measured across the full Agent Retrieval Bench evaluation suite: ### Part E: System Architecture & Deployment Status - **CPU Native Architecture**: RepoNeedle runs entirely in Python/NumPy/SciPy/NetworkX without requiring CUDA GPUs or heavy deep learning runtime dependencies. -- **Release Status**: **V1.1.0 source integrated on main; formal release publication pending.** Features default `legacy` ranker with backwards-compatible contract preservation, and opt-in `change_impact` ranking engine for seed-relative relationship scoring. +- **Release Status**: **1.1.0 is published on PyPI (2026-11-03)**, with the default `legacy` ranker and the opt-in `change_impact` ranker. The development branch adds the features listed in [Release Status](#-release-status) and is planned for 1.2.0; it is **not yet released**. +- **Abstention during these evaluations**: Parts A–C, the Part D latency table and Part F were measured with code in which the abstention heuristic was always on. A full re-run on ARB v2 (2026-11-09) verified that the heuristic would abstain on **0 of 345** positive queries and that selected files are identical with and without it, so the new opt-in default does not change any ARB v2 result. See [docs/BENCHMARK_CONFIGURATION.md](docs/BENCHMARK_CONFIGURATION.md). --- @@ -182,113 +484,25 @@ Statistical inference was conducted using **repository-clustered statistical mod --- -## 🚀 Quickstart & Installation - -RepoNeedle is self-contained and requires standard Python dependencies (`numpy`, `scipy`, `networkx`, `pydantic`, `pathspec`, `rich`, and `tomli` on Python < 3.11). - -### 1. Installation - -```bash -# Clone the verified repository -git clone https://github.com/krishika08/RepoNeedle.git -cd RepoNeedle - -# Install package in editable mode -pip install -e . - -# Or install with development and test dependencies -pip install -e ".[dev]" -``` - -> [!TIP] -> On Python 3.10, `tomli>=2.0.1` is automatically installed via conditional dependency. On Python 3.11 and 3.12, native standard library `tomllib` is used with zero extra dependencies. - ---- - -### 2. Python API - -#### Standard Retrieval (Default Legacy Ranker): -```python -from reponeedle import RepoNeedleEngine, BudgetConfig - -# 1. Initialize engine on a local codebase (uses default 'legacy' ranker) -engine = RepoNeedleEngine.from_directory( - repo_dir="./my-project", - budget_config=BudgetConfig(max_tokens=4000, allow_skeletons=True, mode="auto"), -) - -# 2. Retrieve context for a task (auto-resolves to compact mode for <= 4k) -result = engine.retrieve( - query="Add support for blueprint url_prefix inheritance in route registration", - budget_tokens=4000, -) - -if result.abstained: - print(f"Abstained: {result.explanation}") -else: - print(f"Retrieved {len(result.selected_files)} files ({result.total_tokens} / {result.budget_tokens} tokens):") - for f in result.selected_files: - print(f"[{f.tier.value.upper()}] {f.path} (score: {f.score:.3f}, tokens: {f.token_count})") - - # Access the exact fenced-markdown serialized prompt ready for LLM consumption: - llm_prompt_context = result.rendered_context -``` - -#### Opt-In: Seed-Based Change-Impact Retrieval: -```python -from reponeedle import RepoNeedleEngine, Query - -# Initialize engine with Change-Impact ranking -engine = RepoNeedleEngine.from_directory( - repo_dir="./my-project", - ranker="change_impact", -) - -# Populate anchor_files with the seed path to activate relationship scoring -query = Query( - text="Fix authentication session expiration", - anchor_files=["auth/session.py"], - budget_tokens=4000, -) - -result = engine.retrieve(query) - -for f in result.selected_files: - print(f"[{f.tier.value.upper()}] {f.path} (score: {f.score:.3f}, tokens: {f.token_count})") - if f.explanation: - print(f" Attribution: {f.explanation}") -``` - -> [!NOTE] -> **Behavior of Plain-String (Seedless) Change-Impact Queries**: -> The Change-Impact ranking engine reads seed file paths from `query.anchor_files`. When `engine.retrieve()` is called with a plain string query (or without `anchor_files`): -> - **Lexical and semantic scoring remain active**: Candidate files continue to be retrieved and scored using combined BM25 lexical matching and subword semantic retrieval. -> - **Seed suppression is inactive without `anchor_files`**: Seed File Suppression (SFS) is not triggered since there are no designated seed files to down-weight. -> - **Seed-relative relationship boosts are inactive without `anchor_files`**: Relationship scoring across dependencies (static imports, symbols, calls, build manifests, test-stems, and directory proximity) is skipped. -> - **Test Intent Prior adjustments remain available according to detected query intent**: Test files receive boosts or penalties (`tip_multiplier_test` / `tip_multiplier_non_test`) based on detected test intent in the query text. -> -> To activate seed-relative relationship boosts and seed suppression, pass seed paths via `Query(text=..., anchor_files=['path/to/seed.py'])`. - ---- - -### 3. Command Line Interface (CLI) +### Part G: Supplemental Product-Aligned Evaluation (Not an Official ARB Score) -```bash -# Default 'legacy' retrieval under a 4000 token limit -reponeedle "Fix AttributeError in SecureCookieSessionInterface cookie saving" --repo ./flask --budget 4000 +> [!WARNING] +> **Supplemental evaluation, separate from the official results above.** Parts A–C are official ARB v2 harness results. This part uses a supplemental evaluator to answer a different question: how good is RepoNeedle's **actual full ranking** when it reads **whole files**, as it does in real use? -# Opt-in Change-Impact retrieval (applies Test Intent Prior scoring) -reponeedle "fix token expiration in auth/session.py" --repo ./my-project --budget 4000 --ranker change_impact +The official harness gives RepoNeedle a single fragment per file (while its BM25 baseline ranks every chunk) and scores files RepoNeedle did not pack in corpus order. The supplemental evaluation (2026-11-09, current engine) instead ranks **files** for every system and compares RepoNeedle and BM25 on the **same** input representation. It reports full-ranking metrics without remainder positions, and packed-context coverage separately. -# Task-conditioned retrieval for finding regression tests -reponeedle "test suite for HTTPBasicAuth" --repo ./requests --task code2test --budget 2000 +| ARB v2 positive (345 queries, 25 repositories), file-level full ranking | MRR | Recall@5 | Recall@10 | Recall@20 | +| :--- | :---: | :---: | :---: | :---: | +| RepoNeedle, whole-file input | 0.2789 | 0.4270 | 0.5862 | 0.7042 | +| BM25, whole-file input | 0.1717 | 0.2290 | 0.3498 | 0.5138 | +| RepoNeedle, fragment input (official harness input) | 0.2379 | 0.3061 | 0.4407 | 0.5803 | +| BM25, fragment input | 0.1439 | 0.1650 | 0.2389 | 0.3604 | -# Serialize packaged markdown directly to a file -reponeedle "blueprints and route registration" --repo ./flask --budget 4000 --output-file prompt.md -``` +- **Matched whole-file input, overall**: RepoNeedle − BM25 = MRR +0.1073 (repository-clustered 95% CI [+0.0647, +0.1596]); Recall@20 +0.1904 [+0.1114, +0.2715]. +- **By task**: significantly better on `code2test` (every metric) and on `edit2ripple` Recall@10/20. Inconclusive on `trace2code` (only 7 repositories). On `comment2context`, level on MRR but **significantly worse on Recall@20** (−0.0896 [−0.1779, −0.0056]). +- **Packed context** (8,000 tokens): gold-file coverage rises from 38.3% (fragments) to 59.8% (whole files); queries with at least one gold file packed rise from 45.8% to 68.4%. -> [!NOTE] -> **CLI Seed-File Limitation**: The current CLI accepts query text as a positional string and does not currently expose a seed-file argument. Standard CLI queries run with `--ranker change_impact` execute as seedless queries: lexical and semantic scoring remain active, and Test Intent Prior adjustments remain available according to detected query intent, while seed suppression and seed-relative relationship boosts are inactive without `anchor_files`. Full seed-based relationship scoring is available via the Python API (`Query(anchor_files=[...])`). CLI seed-file support is planned as a separate future enhancement. +These figures are not comparable to Part A and do not replace it. Method, fidelity checks (including exact reproduction of the official harness on 345/345 queries) and limitations: [reports/experimental/phase1_product_aligned_evaluation/REPORT.md](reports/experimental/phase1_product_aligned_evaluation/REPORT.md). --- @@ -312,22 +526,27 @@ RepoNeedle integrates the **CUD-Pack** context optimization engine, replacing he ## 🔬 Reproducing Benchmark Results -Run the complete deterministic test suite and benchmarks: - ```bash -# 1. Run automated test suite (271 passed tests locally, 245 in clean CI checkouts) -pytest -q -rs +# 1. Run the automated test suite (the count changes as tests are added; CI reports the current number) +python -m pytest -q -rs -# 2. Run component ablation study with 95% Bootstrap CI +# 2. Re-verify the ARB v2 abstention results and positive-query reproducibility +# (needs external/agent-retrieval-bench; read-only; add --positives for the slower N=345 check) +python scripts/verify_abstention_reproducibility.py + +# 3. Run the component ablation study with 95% bootstrap CI +# (overwrites benchmarks/results/ablation_study_results.json; compare with `git diff`) python benchmarks/run_ablations.py -# 3. Verify actual BPE token payload reduction and latency +# 4. Verify BPE token payload reduction and latency python scripts/verify_token_savings_and_latency.py -# 4. Build distribution packages -python -m build --no-isolation +# 5. Build distribution packages into a scratch directory +python -m build --no-isolation --outdir /tmp/reponeedle-dist ``` +Which scripts enable the experimental abstention heuristic, which do not, and which historical results were measured under the old always-on default are documented in [docs/BENCHMARK_CONFIGURATION.md](docs/BENCHMARK_CONFIGURATION.md). That file also lists known reproducibility gaps; for example, the synthetic `benchmarks/results/benchmark_results.json` from v0.1.0 is not reproduced by later code. + --- ## 📦 Hugging Face Release Artifacts diff --git a/REPONEEDLE_V1_FINAL_DELIVERY.md b/REPONEEDLE_V1_FINAL_DELIVERY.md index c79683e..e15e989 100644 --- a/REPONEEDLE_V1_FINAL_DELIVERY.md +++ b/REPONEEDLE_V1_FINAL_DELIVERY.md @@ -1,4 +1,8 @@ # REPONEEDLE V1.0.0 — FINAL DELIVERY REPORT + +> [!NOTE] +> **Historical record (2026-11-02), describing the 1.0.0 delivery only.** It has not been updated for later versions. The current release is **1.1.0** (PyPI, 2026-11-03); the development branch is planned for **1.2.0** (unreleased). Two statements below are qualified elsewhere: the description of RepoNeedle as "the first" engine of its kind is an unverified priority claim, and the zero-overflow guarantee holds for RepoNeedle's internal regex token model, not for every LLM tokenizer (see the README's tokenizer boundary disclosure). + ## MASTER EXECUTION DIRECTIVE COMPLETION **Final Software Version**: `1.0.0` diff --git a/benchmarks/render_product_aligned_tables.py b/benchmarks/render_product_aligned_tables.py new file mode 100644 index 0000000..4c65728 --- /dev/null +++ b/benchmarks/render_product_aligned_tables.py @@ -0,0 +1,52 @@ +"""Render markdown tables for the supplemental product-aligned evaluation from its JSON outputs. + +Reads reports/experimental/phase1_product_aligned_evaluation/{aggregate,confidence_intervals,fidelity}.json +and writes TABLES.md next to them, so every number in REPORT.md is traceable to machine-readable output. +""" + +import json +from pathlib import Path + +OUT = Path(__file__).resolve().parent.parent / "reports" / "experimental" / "phase1_product_aligned_evaluation" +GROUPS = ["overall", "comment2context", "code2test", "trace2code", "edit2ripple"] +SYS_LABEL = { + "rn_fragment": "RepoNeedle, fragment input", + "rn_wholefile": "RepoNeedle, whole-file input", + "bm25_fragment": "BM25, fragment input (file unit)", + "bm25_wholefile": "BM25, whole-file input (file unit)", + "bm25_chunks": "BM25, all chunks → file by max (official baseline)", +} +METRICS = ["mrr", "recall@5", "recall@10", "recall@20"] + + +def main(): + agg = json.loads((OUT / "aggregate.json").read_text(encoding="utf-8")) + cis = json.loads((OUT / "confidence_intervals.json").read_text(encoding="utf-8")) + lines = ["# Tables (generated from aggregate.json and confidence_intervals.json)", ""] + for g in GROUPS: + a = agg[g] + lines += [f"## {g} (queries = {a['n_queries']}, repositories = {a['n_repositories']})", "", + "| System | MRR | Recall@5 | Recall@10 | Recall@20 |", "| :--- | :---: | :---: | :---: | :---: |"] + for s, label in SYS_LABEL.items(): + lines.append(f"| {label} | " + " | ".join(f"{a[s][m]:.4f}" for m in METRICS) + " |") + lines += ["", "| Packed context (CUD-Pack, 8,000 tokens) | Files packed | Utilisation | Gold coverage | Queries with ≥1 gold packed | Unselected files |", + "| :--- | :---: | :---: | :---: | :---: | :---: |"] + for rep in ["fragment", "wholefile"]: + p = a[f"packed_{rep}"] + lines.append(f"| {rep} input | {p['n_files']:.1f} | {p['utilisation']:.1%} | {p['gold_coverage']:.1%} | {p['success']:.1%} | {p['unselected_files']:.0f} (no positions assigned) |") + lines += ["", "| Comparison (paired, repo-clustered 95% CI) | Metric | Mean Δ | W / T / L | 95% CI | CI excludes 0 |", + "| :--- | :--- | :---: | :---: | :---: | :---: |"] + for c in cis[g]: + lines.append(f"| {c['label']} | {c['metric']} | {c['mean_diff']:+.4f} | {c['wins']} / {c['ties']} / {c['losses']} | " + f"[{c['ci95'][0]:+.4f}, {c['ci95'][1]:+.4f}] | {'**yes**' if c['excludes_zero'] else 'no'} |") + lat = a["latency_ms"] + lines += ["", "| Latency per query (ms, 4 concurrent shards) | Mean | p50 |", "| :--- | :---: | :---: |"] + for k, v in lat.items(): + lines.append(f"| {k} | {v['mean']:.1f} | {v['p50']:.1f} |") + lines.append("") + (OUT / "TABLES.md").write_text("\n".join(lines), encoding="utf-8") + print(f"wrote {OUT / 'TABLES.md'}") + + +if __name__ == "__main__": + main() diff --git a/benchmarks/run_ablations.py b/benchmarks/run_ablations.py index 4d123fa..966614a 100644 --- a/benchmarks/run_ablations.py +++ b/benchmarks/run_ablations.py @@ -72,7 +72,12 @@ def __init__( self.disable_abstainer = disable_abstainer self.disable_skeletons = disable_skeletons - config = BudgetConfig(max_tokens=4000, allow_skeletons=not disable_skeletons) + # Explicit opt-in (1.2.0+): the "abstainer" ablation factor measures the experimental heuristic. + config = BudgetConfig( + max_tokens=4000, + allow_skeletons=not disable_skeletons, + enable_experimental_abstention=True, + ) self.engine = RepoNeedleEngine.from_documents(docs, budget_config=config) def retrieve(self, sample: BenchmarkSample, budget_tokens: int = 4000): diff --git a/benchmarks/run_arb_comprehensive_validation.py b/benchmarks/run_arb_comprehensive_validation.py index e561929..2b153de 100644 --- a/benchmarks/run_arb_comprehensive_validation.py +++ b/benchmarks/run_arb_comprehensive_validation.py @@ -153,7 +153,10 @@ def run_reponeedle_variant( is_test = "test" in path.lower() docs.append(FileDoc(path=path, content=text, is_test=is_test)) - engine = RepoNeedleEngine.from_documents(docs) + # Explicit opt-in (1.2.0+) preserves the historical evaluation semantics. + engine = RepoNeedleEngine.from_documents( + docs, budget_config=BudgetConfig(enable_experimental_abstention=True) + ) # Apply ablation modifications raw_task = sample.get("task_type", "auto") @@ -435,7 +438,10 @@ def evaluate_abstention_pilot() -> Dict[str, Any]: # Fallback: synthesize dummy candidate from repo name docs = [FileDoc(path=f"{repo}/src/main.py", content=f"# {repo} main module")] - engine = RepoNeedleEngine.from_documents(docs) + # Explicit opt-in (1.2.0+): this selective evaluation measures the experimental heuristic. + engine = RepoNeedleEngine.from_documents( + docs, budget_config=BudgetConfig(enable_experimental_abstention=True) + ) res = engine.retrieve(query=q_text, budget_tokens=8000) should_abstain = res.abstained diff --git a/benchmarks/run_arb_current_engine.py b/benchmarks/run_arb_current_engine.py new file mode 100644 index 0000000..1a784b3 --- /dev/null +++ b/benchmarks/run_arb_current_engine.py @@ -0,0 +1,224 @@ +""" +Re-measure the *current* RepoNeedle engine on the ARB v2 positive split (N=345). + +Why this exists +--------------- +README Part A was produced on 2026-10-28 by `benchmarks/run_complete_arb_evaluation.py` with the +engine at commit 8e00425 (tag `v0.1.0-baseline`). Later engine changes (before the 1.1.0 release) +changed RepoNeedle's rankings, so those numbers do not describe 1.1.0 or later. This script measures +RepoNeedle only, replicating that harness's RepoNeedle path exactly (document construction, +task-type conditioning, budget 8,000, chunk ordering, ARB `sample_metrics` and `pack_files`). +The official BM25 / Lexical / RepoMap baselines do not depend on RepoNeedle code, so their +historical per-sample values are reused for comparison. + +Outputs go to a NEW directory (default `reports/arb_v2_current_engine/`). Historical reports in +`reports/full_arb_validation/` are only read, never written. + +Usage +----- + # run shards in parallel (each resumable), then merge + python benchmarks/run_arb_current_engine.py --shard 0 --num-shards 3 + python benchmarks/run_arb_current_engine.py --shard 1 --num-shards 3 + python benchmarks/run_arb_current_engine.py --shard 2 --num-shards 3 + python benchmarks/run_arb_current_engine.py --merge + +Validation: setting REPONEEDLE_CODE_ROOT to a checkout of tag `v0.1.0-baseline` must reproduce the +historical per-sample RepoNeedle metrics exactly (see docs/BENCHMARK_CONFIGURATION.md). +""" + +import argparse +import json +import os +import sys +import time +from collections import defaultdict +from pathlib import Path + +import numpy as np + +ROOT_DIR = Path(__file__).resolve().parent.parent +ARB_DIR = ROOT_DIR / "external" / "agent-retrieval-bench" +HIST_DIR = ROOT_DIR / "reports" / "full_arb_validation" +DEFAULT_OUT = ROOT_DIR / "reports" / "arb_v2_current_engine" +CODE_ROOT = Path(os.environ.get("REPONEEDLE_CODE_ROOT", str(ROOT_DIR))) +sys.path.insert(0, str(ARB_DIR / "src")) +sys.path.insert(0, str(CODE_ROOT)) + +SUBSETS = ["code2test", "comment2context", "trace2code", "edit2ripple"] +METRICS = ["mrr", "recall@5", "recall@10", "recall@20", "bcy@4k", "bcy@8k", "bcy@16k", "bcy@32k"] +BCY_BUDGETS = (4000, 8000, 16000, 32000) + + +def _jsonl(path): + return [json.loads(l) for l in open(path, encoding="utf-8") if l.strip()] + + +def run_shard(shard: int, num_shards: int, out_dir: Path, limit_snapshots=None) -> None: + from agent_retrieval_bench.baseline import ( + read_jsonl, target_gold_files, hard_negative_files, query_text_for_eval, + sample_metrics, unique_ranked_paths, + ) + from agent_retrieval_bench.bcy_curve import pack_files, CorpusFileCache + import reponeedle + from reponeedle.engine import RepoNeedleEngine + from reponeedle.models import FileDoc, TaskType + + manifest = {} + for row in _jsonl(ARB_DIR / "data" / "corpus" / "v2_selective_mixed" / "corpus_manifest.jsonl"): + manifest[(row["repo"], row["base_commit"])] = ARB_DIR / row["chunks_path"] + corpus_cache = CorpusFileCache({k: Path(v) for k, v in manifest.items()}) + + samples = [] + for name in SUBSETS: + for r in read_jsonl(ARB_DIR / "data" / "benchmark" / f"v2_{name}" / "samples.jsonl"): + r["_subset_name"] = name + samples.append(r) + by_corpus = defaultdict(list) + for s in samples: + by_corpus[(s["repo"], s["base_commit"])].append(s) + keys = sorted(by_corpus)[: limit_snapshots or None] + mine = [k for i, k in enumerate(keys) if i % num_shards == shard] + + out_dir.mkdir(parents=True, exist_ok=True) + out_path = out_dir / f"shard_{shard}_of_{num_shards}.jsonl" + done = {r["sample_id"] for r in _jsonl(out_path)} if out_path.exists() else set() + enum = {"code2test": TaskType.CODE2TEST, "trace2code": TaskType.TRACE2CODE, "edit2ripple": TaskType.EDIT2RIPPLE} + version = getattr(reponeedle, "__version__", "unknown") + t0 = time.time() + + for n, key in enumerate(mine, start=1): + group = [s for s in by_corpus[key] if s["id"] not in done and target_gold_files(s)] + cpath = manifest.get(key) + if not group or not cpath or not cpath.is_file(): + continue + chunks = read_jsonl(cpath) + file_texts = {} + for c in chunks: + p, t = c.get("path"), c.get("text", "") + if p and (p not in file_texts or len(t) > len(file_texts[p])): + file_texts[p] = t + docs = [FileDoc(path=p, content=t[:25000], is_test="test" in p.lower()) for p, t in file_texts.items()] + t_idx = time.perf_counter() + engine = RepoNeedleEngine.from_documents(docs) + index_ms = (time.perf_counter() - t_idx) * 1000 + path_to_chunks = defaultdict(list) + for c in chunks: + if c.get("path"): + path_to_chunks[c["path"]].append(c) + + with open(out_path, "a", encoding="utf-8") as f: + for s in group: + task = s.get("task_type", s["_subset_name"]) + gold = target_gold_files(s) + t_q = time.perf_counter() + res = engine.retrieve(query=query_text_for_eval(s), budget_tokens=8000, + task_type=enum.get(task, TaskType.COMMENT2CONTEXT)) + latency_ms = (time.perf_counter() - t_q) * 1000 + ranked, seen = [], set() + for sf in res.selected_files: + if sf.path in path_to_chunks and sf.path not in seen: + ranked.extend(path_to_chunks[sf.path]) + seen.add(sf.path) + for p, chs in path_to_chunks.items(): + if p not in seen: + ranked.extend(chs) + seen.add(p) + m = sample_metrics(gold, ranked, hard_negative_files=hard_negative_files(s)) + top = unique_ranked_paths(ranked)[:20] + bcy = {b: pack_files(key[0], key[1], top, set(gold), corpus_cache, b)["bcy"] for b in BCY_BUDGETS} + f.write(json.dumps({ + "sample_id": s["id"], "task_type": task, "repo": key[0], "base_commit": key[1], + "engine_version": version, "abstained": bool(res.abstained), + "mrr": m["MRR"], "recall@5": m["Recall@5"], "recall@10": m["Recall@10"], + "recall@20": m["Recall@20"], "bcy@4k": bcy[4000], "bcy@8k": bcy[8000], + "bcy@16k": bcy[16000], "bcy@32k": bcy[32000], "top_files": top, + "latency_ms": latency_ms, "index_ms": index_ms, + }) + "\n") + corpus_cache._cache.clear() + del engine, docs, chunks, file_texts, path_to_chunks + print(f"[shard {shard}/{num_shards}] {n}/{len(mine)} snapshots, {time.time() - t0:.0f}s", + file=sys.stderr, flush=True) + + +def _boot_ci(diffs, groups, n_boot=5000, seed=0): + rng = np.random.default_rng(seed) + diffs = np.asarray(diffs, dtype=float) + sample = [float(np.mean(rng.choice(diffs, size=len(diffs), replace=True))) for _ in range(n_boot)] + by = defaultdict(list) + for d, g in zip(diffs, groups): + by[g].append(d) + clusters = list(by.values()) + clustered = [] + for _ in range(n_boot): + pick = rng.integers(0, len(clusters), size=len(clusters)) + clustered.append(float(np.mean(np.concatenate([clusters[i] for i in pick])))) + return ([float(np.percentile(sample, 2.5)), float(np.percentile(sample, 97.5))], + [float(np.percentile(clustered, 2.5)), float(np.percentile(clustered, 97.5))]) + + +def merge(out_dir: Path) -> dict: + rows = {} + for p in sorted(out_dir.glob("shard_*.jsonl")): + for r in _jsonl(p): + rows[r["sample_id"]] = r + hist = {r["sample_id"]: r for r in _jsonl(HIST_DIR / "checkpoint_positive.jsonl")} + ids = sorted(set(rows) & set(hist)) + systems = { + "reponeedle_current": lambda i: rows[i], + "reponeedle_2026_10_28": lambda i: hist[i]["reponeedle"], + "bm25": lambda i: hist[i]["bm25"], "lexical": lambda i: hist[i]["lexical"], + "repomap": lambda i: hist[i]["repomap"], + } + + def agg(sel): + return {s: {m: float(np.mean([get(i)[m] for i in sel])) for m in METRICS} for s, get in systems.items()} + + summary = { + "n_samples": len(ids), "n_expected": len(hist), + "engine_versions": sorted({rows[i]["engine_version"] for i in ids}), + "abstained_count": sum(rows[i]["abstained"] for i in ids), + "overall": agg(ids), + "by_task": {t: dict(n=len(sel), **agg(sel)) for t in SUBSETS + for sel in [[i for i in ids if rows[i]["task_type"] == t]] if sel}, + "latency_ms": {"mean": float(np.mean([rows[i]["latency_ms"] for i in ids])), + "p50": float(np.median([rows[i]["latency_ms"] for i in ids])), + "p95": float(np.percentile([rows[i]["latency_ms"] for i in ids], 95))}, + "paired_mrr": {}, + "top20_identical_to_2026_10_28": sum(rows[i]["top_files"] == hist[i]["reponeedle"]["top_files"] for i in ids), + } + repos = [rows[i]["repo"] for i in ids] + cur = np.array([rows[i]["mrr"] for i in ids]) + for other in ["bm25", "lexical", "repomap", "reponeedle_2026_10_28"]: + o = np.array([systems[other](i)["mrr"] for i in ids]) + d = cur - o + ci, cci = _boot_ci(d, repos) + summary["paired_mrr"][other] = { + "mean_diff": float(d.mean()), "wins": int((d > 0).sum()), "ties": int((d == 0).sum()), + "losses": int((d < 0).sum()), "sample_ci95": ci, "repo_clustered_ci95": cci, + } + (out_dir / "SUMMARY.json").write_text(json.dumps(summary, indent=2), encoding="utf-8") + return summary + + +def main(argv=None): + ap = argparse.ArgumentParser(description="Re-measure the current RepoNeedle engine on ARB v2 positives.") + ap.add_argument("--shard", type=int, default=0) + ap.add_argument("--num-shards", type=int, default=1) + ap.add_argument("--out-dir", type=Path, default=DEFAULT_OUT) + ap.add_argument("--limit-snapshots", type=int, default=None, help="Validation only: first N snapshots.") + ap.add_argument("--merge", action="store_true", help="Merge shard outputs and write SUMMARY.json.") + args = ap.parse_args(argv) + if args.out_dir.resolve() == HIST_DIR.resolve(): + ap.error("refusing to write into the historical report directory") + if not (ARB_DIR / "src").exists(): + print(f"Missing ARB checkout at {ARB_DIR}", file=sys.stderr) + return 2 + if args.merge: + print(json.dumps(merge(args.out_dir), indent=2)) + else: + run_shard(args.shard, args.num_shards, args.out_dir, args.limit_snapshots) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/benchmarks/run_arb_evaluator_audit.py b/benchmarks/run_arb_evaluator_audit.py new file mode 100644 index 0000000..8993b7e --- /dev/null +++ b/benchmarks/run_arb_evaluator_audit.py @@ -0,0 +1,364 @@ +""" +Phase 0 diagnostic: does the official ARB v2 harness measure RepoNeedle's full retrieval ranking? + +EXPERIMENTAL DIAGNOSTIC. This does not replace or modify the official evaluation +(`benchmarks/run_complete_arb_evaluation.py`, `benchmarks/run_arb_current_engine.py`) or the +official ARB metric definitions. Outputs go to `reports/experimental/phase0_evaluator_audit/`. + +For every ARB v2 positive query it records, for RepoNeedle and the official BM25 baseline, using +the same repository snapshot, candidate universe (files present in the snapshot's chunks), query +text, task label and gold files: + + OFFICIAL The sequence the official harness evaluates for RepoNeedle: files packed by CUD-Pack + (in packed order), followed by all remaining files in corpus (chunk-file) order. + VIEW A Full-ranking metrics: the complete ranking produced by the engine before packing + (exactly the list handed to CUD-Pack), with fused and per-signal scores. For BM25, + the official chunk ranking collapsed to unique files (best chunk score per file). + VIEW B Packed-context metrics: gold coverage of the files CUD-Pack actually delivered, + success, budget utilisation; plus ARB `pack_files` BCY@8k applied to each full ranking. + VIEW C Remainder diagnostics: which gold files were not packed, their position in the + official sequence (corpus order) vs. in the full ranking, and zero-score tails. + +File-level MRR / Recall@K use the official ARB helpers (`reciprocal_rank`, `recall_at`). + +Document modes (--doc-mode): + official RepoNeedle documents built exactly as the official harness does: the single longest + ARB chunk per path, truncated to 25,000 characters (default). + reconstructed DIAGNOSTIC ONLY: each file rebuilt from its ARB chunks (the `file` chunk's header + lines before the first symbol, then every `symbol` chunk in line order), approximating + the full file text RepoNeedle reads in real use. Written to a separate subdirectory. +Both modes also record ARB's own BM25 applied to whole-file pseudo-chunks of each representation +("bm25_file_longest_chunk", "bm25_file_reconstructed") to separate granularity from text coverage. + +Usage: + python benchmarks/run_arb_evaluator_audit.py --shard 0 --num-shards 3 # (x3, parallel) + python benchmarks/run_arb_evaluator_audit.py --merge + python benchmarks/run_arb_evaluator_audit.py --trace [ ...] +""" + +import argparse +import json +import sys +import time +from collections import defaultdict +from pathlib import Path + +import numpy as np + +ROOT_DIR = Path(__file__).resolve().parent.parent +ARB_DIR = ROOT_DIR / "external" / "agent-retrieval-bench" +OUT_DIR = ROOT_DIR / "reports" / "experimental" / "phase0_evaluator_audit" +CURRENT_DIR = ROOT_DIR / "reports" / "arb_v2_current_engine" +HIST_DIR = ROOT_DIR / "reports" / "full_arb_validation" +sys.path.insert(0, str(ARB_DIR / "src")) +sys.path.insert(0, str(ROOT_DIR)) + +SUBSETS = ["code2test", "comment2context", "trace2code", "edit2ripple"] +KS = (5, 10, 20) +BUDGET = 8000 + + +def _jsonl(path): + return [json.loads(l) for l in open(path, encoding="utf-8") if l.strip()] + + +def _file_metrics(gold, ranked_paths, recall_at, reciprocal_rank): + g = set(gold) + out = {"mrr": reciprocal_rank(g, ranked_paths)} + for k in KS: + out[f"recall@{k}"] = recall_at(g, ranked_paths, k) + return out + + +def _load(): + from agent_retrieval_bench.baseline import read_jsonl + manifest = {} + for row in _jsonl(ARB_DIR / "data" / "corpus" / "v2_selective_mixed" / "corpus_manifest.jsonl"): + manifest[(row["repo"], row["base_commit"])] = ARB_DIR / row["chunks_path"] + samples = [] + for name in SUBSETS: + for r in read_jsonl(ARB_DIR / "data" / "benchmark" / f"v2_{name}" / "samples.jsonl"): + r["_subset_name"] = name + samples.append(r) + by_corpus = defaultdict(list) + for s in samples: + by_corpus[(s["repo"], s["base_commit"])].append(s) + return manifest, by_corpus + + +def reconstruct_file(path_chunks): + """Header lines of the `file` chunk before the first symbol, then all symbol chunks in line order.""" + file_c = [c for c in path_chunks if c.get("kind") == "file"] + syms = sorted((c for c in path_chunks if c.get("kind") == "symbol"), key=lambda c: c.get("start_line", 0)) + if not syms: + return max((c.get("text", "") for c in path_chunks), key=len, default="") + header = "" + if file_c: + header = "\n".join(file_c[0].get("text", "").splitlines()[: max(0, syms[0].get("start_line", 1) - 1)]) + return header + "\n" + "\n".join(c.get("text", "") for c in syms) + + +def evaluate_snapshot(key, group, manifest, corpus_cache, trace_ids=None, doc_mode="official"): + """Yields one record per sample (and full traces for ids in trace_ids).""" + from agent_retrieval_bench.baseline import ( + read_jsonl, target_gold_files, query_text_for_eval, unique_ranked_paths, + rank_chunks_bm25_with_scores, recall_at, reciprocal_rank, + ) + from agent_retrieval_bench.bcy_curve import pack_files + from reponeedle.engine import RepoNeedleEngine + from reponeedle.models import FileDoc, TaskType, Query + from reponeedle.core.router import WorkflowRouter + + chunks = read_jsonl(manifest[key]) + file_texts = {} + for c in chunks: + p, t = c.get("path"), c.get("text", "") + if p and (p not in file_texts or len(t) > len(file_texts[p])): + file_texts[p] = t + path_to_chunks = defaultdict(list) + for c in chunks: + if c.get("path"): + path_to_chunks[c["path"]].append(c) + reconstructed = {p: reconstruct_file(cs) for p, cs in path_to_chunks.items()} + if doc_mode == "official": + docs = [FileDoc(path=p, content=t[:25000], is_test="test" in p.lower()) for p, t in file_texts.items()] + else: + docs = [FileDoc(path=p, content=reconstructed[p], is_test="test" in p.lower()) for p in path_to_chunks] + engine = RepoNeedleEngine.from_documents(docs) + pseudo_longest = [{"path": p, "chunk_id": p, "text": t} for p, t in file_texts.items()] + pseudo_recon = [{"path": p, "chunk_id": p, "text": t} for p, t in reconstructed.items()] + corpus_order = list(path_to_chunks) # official remainder order + universe = set(corpus_order) + + # Capture the exact ranking handed to CUD-Pack (all files, rank order, with scores). + captured = {} + original_pack = engine.budget_optimizer.pack_context + + def capturing_pack(candidate_files, *a, **kw): + captured["ranking"] = [(d.path, float(score), {k: float(v) for k, v in cs.items()}) + for d, score, cs in candidate_files] + return original_pack(candidate_files, *a, **kw) + + engine.budget_optimizer.pack_context = capturing_pack + router = WorkflowRouter() + enum = {"code2test": TaskType.CODE2TEST, "trace2code": TaskType.TRACE2CODE, "edit2ripple": TaskType.EDIT2RIPPLE} + fm = lambda gold, paths: _file_metrics(gold, paths, recall_at, reciprocal_rank) + bcy8 = lambda paths, gold: pack_files(key[0], key[1], paths[:20], set(gold), corpus_cache, BUDGET)["bcy"] + + for s in group: + gold = target_gold_files(s) + if not gold: + continue + task = s.get("task_type", s["_subset_name"]) + q = query_text_for_eval(s) + captured.clear() + res = engine.retrieve(query=q, budget_tokens=BUDGET, task_type=enum.get(task, TaskType.COMMENT2CONTEXT)) + ranking = [r for r in captured.get("ranking", []) if r[0] in universe] + full_paths = [p for p, _, _ in ranking] + score_of = {p: sc for p, sc, _ in ranking} + packed = [sf.path for sf in res.selected_files if sf.path in universe] + packed_set = set(packed) + official = packed + [p for p in corpus_order if p not in packed_set] + + # BM25 (official baseline): chunk ranking collapsed to files, best chunk score per file. + bm25_ranked = rank_chunks_bm25_with_scores(q, chunks) + bm25_paths = unique_ranked_paths([c for _, c in bm25_ranked]) + bm25_score = {} + for sc, c in bm25_ranked: + bm25_score.setdefault(str(c.get("path", "")), float(sc)) + + bm25_file_longest = [str(c["path"]) for _, c in rank_chunks_bm25_with_scores(q, pseudo_longest)] + bm25_file_recon = [str(c["path"]) for _, c in rank_chunks_bm25_with_scores(q, pseudo_recon)] + lex_paths = [p for p, _, _ in sorted(ranking, key=lambda r: (-r[2].get("lexical", 0.0), r[0]))] + sem_paths = [p for p, _, _ in sorted(ranking, key=lambda r: (-r[2].get("semantic", 0.0), r[0]))] + pos = lambda paths: {p: i for i, p in enumerate(paths, start=1)} + p_full, p_off, p_bm25 = pos(full_paths), pos(official), pos(bm25_paths) + + gold_detail = [] + for g in gold: + gold_detail.append({ + "path": g, "in_universe": g in universe, + "packed": g in packed_set, + "official_rank": p_off.get(g), "full_rank": p_full.get(g), "bm25_rank": p_bm25.get(g), + "rn_score": score_of.get(g), "bm25_score": bm25_score.get(g), + }) + off_top20 = set(official[:20]); full_top20 = set(full_paths[:20]); gs = set(gold) + rec = { + "sample_id": s["id"], "task_type": task, "repo": key[0], "base_commit": key[1], + "n_files": len(universe), "n_gold": len(gold), + "auto_route": router.classify_intent(Query(text=q))[0].value, + "official": fm(gold, official), + "rn_full": fm(gold, full_paths), + "rn_lexical_only": fm(gold, lex_paths), + "rn_semantic_only": fm(gold, sem_paths), + "bm25_full": fm(gold, bm25_paths), + "bm25_file_longest_chunk": fm(gold, bm25_file_longest), + "bm25_file_reconstructed": fm(gold, bm25_file_recon), + "doc_mode": doc_mode, + "packed": { + "n_files": len(packed), "tokens": int(res.total_tokens), + "utilisation": res.total_tokens / BUDGET, + "gold_coverage": len(gs & packed_set) / len(gs), + "success": bool(gs & packed_set), + "packed_full_rank_max": max((p_full.get(p, 0) for p in packed), default=0), + "skipped_in_full_top_k": sum(1 for p in full_paths[:len(packed)] if p not in packed_set), + }, + "bcy8k": {"rn_official_seq": bcy8(official, gold), "rn_full_ranking": bcy8(full_paths, gold), + "bm25": bcy8(bm25_paths, gold)}, + "remainder": { + "gold_not_packed": sum(1 for g in gold if g not in packed_set), + "official_top20_hits_from_remainder": len((off_top20 - packed_set) & gs), + "full_top20_hits_lost_officially": len((full_top20 - off_top20) & gs), + "rn_gold_zero_score": sum(1 for g in gold if score_of.get(g, 0.0) <= 0.0), + "bm25_gold_zero_score": sum(1 for g in gold if bm25_score.get(g, 0.0) <= 0.0), + "rn_zero_score_files": sum(1 for _, sc, _ in ranking if sc <= 0.0), + "bm25_zero_score_files": sum(1 for p in bm25_paths if bm25_score.get(p, 0.0) <= 0.0), + }, + "gold_detail": gold_detail, + } + if trace_ids and s["id"] in trace_ids: + rec["trace"] = { + "query": q, + "rn_full_top25": [{"rank": i, "path": p, "fused": round(sc, 4), + **{k: round(v, 4) for k, v in cs.items()}, "gold": p in gs, "packed": p in packed_set} + for i, (p, sc, cs) in enumerate(ranking[:25], start=1)], + "packed": [{"order": i, "path": sf.path, "tier": sf.tier.value, "tokens": sf.token_count, + "full_rank": p_full.get(sf.path), "gold": sf.path in gs} + for i, sf in enumerate(res.selected_files, start=1)], + "official_top25": [{"rank": i, "path": p, "source": "packed" if p in packed_set else "corpus-order", + "gold": p in gs} for i, p in enumerate(official[:25], start=1)], + "bm25_top25": [{"rank": i, "path": p, "score": round(bm25_score.get(p, 0.0), 4), "gold": p in gs} + for i, p in enumerate(bm25_paths[:25], start=1)], + } + yield rec + + engine.budget_optimizer.pack_context = original_pack + + +def _out_dir(doc_mode): + return OUT_DIR if doc_mode == "official" else OUT_DIR / "reconstructed_docs" + + +def run(shard, num_shards, trace_ids=None, only_ids=None, doc_mode="official"): + from agent_retrieval_bench.bcy_curve import CorpusFileCache + manifest, by_corpus = _load() + corpus_cache = CorpusFileCache({k: Path(v) for k, v in manifest.items()}) + out_dir = _out_dir(doc_mode) + out_dir.mkdir(parents=True, exist_ok=True) + if only_ids: + keys = sorted({k for k, g in by_corpus.items() for s in g if s["id"] in only_ids}) + out_path = out_dir / "traces.jsonl" + mode = "w" + else: + keys = [k for i, k in enumerate(sorted(by_corpus)) if i % num_shards == shard] + out_path = out_dir / f"shard_{shard}_of_{num_shards}.jsonl" + mode = "a" + done = set() if only_ids or not out_path.exists() else {r["sample_id"] for r in _jsonl(out_path)} + t0 = time.time() + with open(out_path, mode, encoding="utf-8") as f: + for n, key in enumerate(keys, start=1): + group = [s for s in by_corpus[key] if s["id"] not in done and (not only_ids or s["id"] in only_ids)] + if not group or not manifest.get(key) or not manifest[key].is_file(): + continue + for rec in evaluate_snapshot(key, group, manifest, corpus_cache, trace_ids, doc_mode): + f.write(json.dumps(rec) + "\n") + f.flush() + corpus_cache._cache.clear() + print(f"[audit {shard}/{num_shards}] {n}/{len(keys)} snapshots, {time.time() - t0:.0f}s", + file=sys.stderr, flush=True) + + +def _clustered_ci(diffs, groups, n_boot=5000, seed=0): + rng = np.random.default_rng(seed) + by = defaultdict(list) + for d, g in zip(diffs, groups): + by[g].append(d) + clusters = list(by.values()) + boots = [float(np.mean(np.concatenate([clusters[i] for i in rng.integers(0, len(clusters), len(clusters))]))) + for _ in range(n_boot)] + return [float(np.percentile(boots, 2.5)), float(np.percentile(boots, 97.5))] + + +def merge(doc_mode="official"): + out_dir = _out_dir(doc_mode) + rows = {} + for p in sorted(out_dir.glob("shard_*.jsonl")): + for r in _jsonl(p): + rows[r["sample_id"]] = r + official_ref = {} + for p in sorted(CURRENT_DIR.glob("shard_*.jsonl")): + for r in _jsonl(p): + official_ref[r["sample_id"]] = r + hist = {r["sample_id"]: r for r in _jsonl(HIST_DIR / "checkpoint_positive.jsonl")} + ids = sorted(rows) + mean = lambda vals: float(np.mean(vals)) if vals else 0.0 + + # Fidelity checks: recomputed official sequence vs. the official current-engine run, and + # recomputed BM25 vs. the historical official BM25 per-sample metrics. + keys = ["mrr", "recall@5", "recall@10", "recall@20"] + off_match = sum(all(abs(rows[i]["official"][k] - official_ref[i][k]) < 1e-12 for k in keys) + for i in ids if i in official_ref) + bm25_match = sum(all(abs(rows[i]["bm25_full"][k] - hist[i]["bm25"][k]) < 1e-12 for k in keys) + for i in ids if i in hist) + bcy_match = sum(abs(rows[i]["bcy8k"]["bm25"] - hist[i]["bm25"]["bcy@8k"]) < 1e-12 for i in ids if i in hist) + + def block(sel): + out = {"n": len(sel)} + for view in ["official", "rn_full", "rn_lexical_only", "rn_semantic_only", "bm25_full", + "bm25_file_longest_chunk", "bm25_file_reconstructed"]: + if all(view in rows[i] for i in sel): + out[view] = {k: mean([rows[i][view][k] for i in sel]) for k in keys} + out["packed"] = {k: mean([float(rows[i]["packed"][k]) for i in sel]) + for k in ["n_files", "utilisation", "gold_coverage", "success", "skipped_in_full_top_k"]} + out["bcy8k"] = {k: mean([rows[i]["bcy8k"][k] for i in sel]) for k in ["rn_official_seq", "rn_full_ranking", "bm25"]} + out["remainder"] = {k: int(sum(rows[i]["remainder"][k] for i in sel)) + for k in ["gold_not_packed", "official_top20_hits_from_remainder", + "full_top20_hits_lost_officially", "rn_gold_zero_score", "bm25_gold_zero_score"]} + out["n_gold"] = int(sum(rows[i]["n_gold"] for i in sel)) + counts = defaultdict(int) + for i in sel: + counts[rows[i]["auto_route"]] += 1 + out["auto_route"] = dict(counts) + repos = [rows[i]["repo"] for i in sel] + out["paired"] = {} + for a, b, k in [("rn_full", "bm25_full", "mrr"), ("rn_full", "bm25_full", "recall@20"), + ("official", "bm25_full", "mrr"), ("rn_full", "official", "mrr"), + ("rn_full", "official", "recall@20")]: + d = np.array([rows[i][a][k] - rows[i][b][k] for i in sel]) + out["paired"][f"{a}-{b}:{k}"] = {"mean": float(d.mean()), "wins": int((d > 0).sum()), + "ties": int((d == 0).sum()), "losses": int((d < 0).sum()), + "repo_clustered_ci95": _clustered_ci(d, repos)} + return out + + summary = { + "doc_mode": doc_mode, + "n_samples": len(ids), + "fidelity": {"official_sequence_reproduces_current_engine_run": f"{off_match}/{len(ids)}", + "bm25_reproduces_historical_official_bm25": f"{bm25_match}/{len(ids)}", + "bm25_bcy8k_reproduces_historical": f"{bcy_match}/{len(ids)}"}, + "overall": block(ids), + "by_task": {t: block([i for i in ids if rows[i]["task_type"] == t]) for t in SUBSETS}, + } + (out_dir / "SUMMARY.json").write_text(json.dumps(summary, indent=2), encoding="utf-8") + return summary + + +def main(argv=None): + ap = argparse.ArgumentParser(description="Phase 0 ARB evaluator audit (experimental diagnostic).") + ap.add_argument("--shard", type=int, default=0) + ap.add_argument("--num-shards", type=int, default=1) + ap.add_argument("--merge", action="store_true") + ap.add_argument("--trace", nargs="+", default=None, help="Write full traces for these sample ids.") + ap.add_argument("--doc-mode", choices=["official", "reconstructed"], default="official") + args = ap.parse_args(argv) + if args.merge: + print(json.dumps(merge(args.doc_mode), indent=2)) + elif args.trace: + run(0, 1, trace_ids=set(args.trace), only_ids=set(args.trace), doc_mode=args.doc_mode) + else: + run(args.shard, args.num_shards, doc_mode=args.doc_mode) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/benchmarks/run_arb_product_aligned_eval.py b/benchmarks/run_arb_product_aligned_eval.py new file mode 100644 index 0000000..5105bc4 --- /dev/null +++ b/benchmarks/run_arb_product_aligned_eval.py @@ -0,0 +1,492 @@ +""" +SUPPLEMENTAL product-aligned evaluation on ARB v2 positives (N=345). + +NOT an official ARB score. The official evaluation (`benchmarks/run_complete_arb_evaluation.py`, +`benchmarks/run_arb_current_engine.py`) and the ARB metric definitions are unchanged. Outputs go to +`reports/experimental/phase1_product_aligned_evaluation/`. + +Protocol (fixed before any results were computed; also written to config.json) +-------------------------------------------------------------------------------- +Retrieval unit FILE for every system. Candidate universe U = every path that has at least one + chunk in the snapshot's ARB chunk file. Gold = ARB `target_gold_files`. +Representations fragment = the single longest ARB chunk for the path (what the official + harness gives RepoNeedle; truncated at 25,000 chars, which never + binds because ARB chunks are capped at 8,000 chars). + wholefile = the path's `file` chunk header lines before the first symbol, + followed by every `symbol` chunk in line order + (`run_arb_evaluator_audit.reconstruct_file`). +Systems (full ranking) rn_fragment RepoNeedle engine, fragment documents + rn_wholefile RepoNeedle engine, whole-file documents + bm25_fragment ARB BM25 over one fragment document per file + bm25_wholefile ARB BM25 over one whole-file document per file + bm25_chunks ARB BM25 over all chunks (the official baseline). Chunk scores + map to files by MAX (best chunk), i.e. first occurrence in the + score-sorted chunk list; this is ARB's own `unique_ranked_paths` + rule, not a choice made here. +RepoNeedle ranking The complete ranking the engine passes to CUD-Pack (all files, fused score). +Ties Score descending, then path ascending (both engines' native order; asserted). +Metrics File-level MRR, Recall@5/10/20 with ARB `reciprocal_rank` / `recall_at`, plus an + independent re-implementation that must agree on every query. +Packed context CUD-Pack output at 8,000 tokens (task label passed, as in the official harness): + gold coverage, success (>=1 gold packed), utilisation, files packed, unselected + file count. Unselected files get NO positions. +Official sequence Packed files then corpus order. Recorded ONLY for fidelity against the official + current-engine run; never used as a full-ranking metric. +Statistics Paired differences per query; repository-clustered bootstrap (resample + repositories with replacement), 10,000 resamples, seed 20261109, 95% percentile CI. + +Usage +----- + python benchmarks/run_arb_product_aligned_eval.py --shard 0 --num-shards 4 # x4 in parallel + python benchmarks/run_arb_product_aligned_eval.py --merge + python benchmarks/run_arb_product_aligned_eval.py --check-determinism 4 # re-run 4 snapshots +""" + +import argparse +import hashlib +import json +import math +import platform +import subprocess +import sys +import time +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np + +ROOT_DIR = Path(__file__).resolve().parent.parent +ARB_DIR = ROOT_DIR / "external" / "agent-retrieval-bench" +OUT_DIR = ROOT_DIR / "reports" / "experimental" / "phase1_product_aligned_evaluation" +CURRENT_DIR = ROOT_DIR / "reports" / "arb_v2_current_engine" +HIST_DIR = ROOT_DIR / "reports" / "full_arb_validation" +PHASE0_DIR = ROOT_DIR / "reports" / "experimental" / "phase0_evaluator_audit" +sys.path.insert(0, str(ARB_DIR / "src")) +sys.path.insert(0, str(ROOT_DIR)) +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +SUBSETS = ["code2test", "comment2context", "trace2code", "edit2ripple"] +SYSTEMS = ["rn_fragment", "rn_wholefile", "bm25_fragment", "bm25_wholefile", "bm25_chunks"] +METRICS = ["mrr", "recall@5", "recall@10", "recall@20"] +KS = (5, 10, 20) +BUDGET = 8000 +TOP_N_STORED = 100 +BOOT_N = 10_000 +BOOT_SEED = 20261109 +COMPARISONS = [ + ("rn_wholefile", "rn_fragment", "Representation effect on RepoNeedle"), + ("bm25_wholefile", "bm25_fragment", "Representation effect on BM25"), + ("rn_fragment", "bm25_fragment", "RepoNeedle vs BM25, matched fragment input"), + ("rn_wholefile", "bm25_wholefile", "RepoNeedle vs BM25, matched whole-file input"), + ("rn_wholefile", "bm25_chunks", "RepoNeedle whole-file vs official chunk BM25 (different BM25 unit)"), +] + +CONFIG = { + "evaluation": "supplemental product-aligned full-ranking evaluation (NOT an official ARB score)", + "dataset": "ARB v2 positive split: v2_code2test, v2_comment2context, v2_trace2code, v2_edit2ripple", + "corpus_manifest": "data/corpus/v2_selective_mixed/corpus_manifest.jsonl", + "retrieval_unit": "file", + "candidate_universe": "paths with >=1 chunk in the snapshot chunk file", + "representations": { + "fragment": "longest ARB chunk per path, [:25000]", + "wholefile": "file-chunk header lines before first symbol + all symbol chunks in line order", + }, + "systems": SYSTEMS, + "chunk_to_file_rule": "max (best chunk score) = ARB unique_ranked_paths order", + "tie_rule": "score desc, path asc", + "metrics": METRICS + ["reciprocal_rank", "gold_positions"], + "packed_context_budget_tokens": BUDGET, + "task_label_passed_to_engine": True, + "stored_ranking_depth": TOP_N_STORED, + "bootstrap": {"type": "paired, repository-clustered percentile", "resamples": BOOT_N, "seed": BOOT_SEED, "ci": 0.95}, + "comparisons": [{"a": a, "b": b, "label": l} for a, b, l in COMPARISONS], +} + + +# --------------------------------------------------------------------------- +# Pure helpers (importable without ARB data; unit-tested) +# --------------------------------------------------------------------------- + +def rank_files(scored): + """[(path, score)] -> paths ordered by score desc, path asc (documented tie rule).""" + return [p for p, _ in sorted(scored, key=lambda x: (-x[1], x[0]))] + + +def collapse_chunks_max(scored_chunks): + """[(path, score)] chunk scores -> [(path, max score)] file scores.""" + best = {} + for p, s in scored_chunks: + if p not in best or s > best[p]: + best[p] = s + return list(best.items()) + + +def file_metrics(gold, ranked_paths): + """Independent implementation of file-level MRR and Recall@K (cross-checked against ARB).""" + g = set(gold) + rr = 0.0 + for i, p in enumerate(ranked_paths, start=1): + if p in g: + rr = 1.0 / i + break + out = {"mrr": rr} + for k in KS: + out[f"recall@{k}"] = len(g & set(ranked_paths[:k])) / len(g) if g else 0.0 + return out + + +def gold_positions(gold, ranked_paths): + pos = {p: i for i, p in enumerate(ranked_paths, start=1)} + return {g: pos.get(g) for g in gold} + + +def clustered_bootstrap_ci(diffs, clusters, n_boot=BOOT_N, seed=BOOT_SEED): + """Paired repository-clustered percentile CI of the mean difference.""" + rng = np.random.default_rng(seed) + by = defaultdict(list) + for d, c in zip(diffs, clusters): + by[c].append(d) + groups = [np.asarray(v, dtype=float) for _, v in sorted(by.items())] + k = len(groups) + boots = np.empty(n_boot) + for b in range(n_boot): + pick = rng.integers(0, k, size=k) + boots[b] = np.concatenate([groups[i] for i in pick]).mean() + return [float(np.percentile(boots, 2.5)), float(np.percentile(boots, 97.5))] + + +class PrecomputedBM25: + """ARB `rank_chunks_bm25_with_scores`, with tokenisation hoisted out of the per-query loop. + Arithmetic is replicated term for term; equality with ARB's function is asserted at runtime.""" + + def __init__(self, chunks, tokenize, chunk_text, k1, b): + self.chunks, self.tokenize, self.k1, self.b = chunks, tokenize, k1, b + self.doc_tokens = [tokenize(chunk_text(c)) for c in chunks] + self.lengths = [len(t) for t in self.doc_tokens] + self.avg = sum(self.lengths) / len(self.lengths) if self.lengths else 0.0 + self.df = Counter() + for t in self.doc_tokens: + self.df.update(set(t)) + self.tf = [Counter(t) for t in self.doc_tokens] + self.total = max(1, len(chunks)) + + def rank(self, query_text): + q = self.tokenize(query_text) + if not q: + return [(0.0, c) for c in self.chunks] + qc = Counter(q) + scored = [] + for chunk, tc, dl in zip(self.chunks, self.tf, self.lengths): + score = 0.0 + for token, query_count in qc.items(): + tf = tc.get(token, 0) + if not tf: + continue + df = self.df[token] + idf = math.log(1.0 + (self.total - df + 0.5) / (df + 0.5)) + length_norm = 1.0 - self.b + if self.avg > 0: + length_norm += self.b * (dl / self.avg) + denominator = tf + self.k1 * length_norm + score += query_count * idf * ((tf * (self.k1 + 1.0)) / denominator) + scored.append((score, str(chunk.get("path", "")), str(chunk.get("chunk_id", "")), chunk)) + scored.sort(key=lambda item: (-item[0], item[1], item[2])) + return [(item[0], item[3]) for item in scored] + + +# --------------------------------------------------------------------------- +# Evaluation +# --------------------------------------------------------------------------- + +def _jsonl(path): + return [json.loads(l) for l in open(path, encoding="utf-8") if l.strip()] + + +def _load(): + from agent_retrieval_bench.baseline import read_jsonl + manifest = {} + for row in _jsonl(ARB_DIR / "data" / "corpus" / "v2_selective_mixed" / "corpus_manifest.jsonl"): + manifest[(row["repo"], row["base_commit"])] = ARB_DIR / row["chunks_path"] + by_corpus = defaultdict(list) + for name in SUBSETS: + for r in read_jsonl(ARB_DIR / "data" / "benchmark" / f"v2_{name}" / "samples.jsonl"): + r["_subset_name"] = name + by_corpus[(r["repo"], r["base_commit"])].append(r) + return manifest, by_corpus + + +def _engine_ranking(engine, query, task_enum, universe): + """Run retrieve(); return (full ranking [(path, fused)], packed paths, result, latency_ms).""" + captured = {} + original = engine.budget_optimizer.pack_context + + def capture(candidate_files, *a, **kw): + captured["r"] = [(d.path, float(s)) for d, s, _ in candidate_files] + return original(candidate_files, *a, **kw) + + engine.budget_optimizer.pack_context = capture + try: + t0 = time.perf_counter() + res = engine.retrieve(query=query, budget_tokens=BUDGET, task_type=task_enum) + latency = (time.perf_counter() - t0) * 1000 + finally: + engine.budget_optimizer.pack_context = original + ranking = captured.get("r", []) + assert [p for p, _ in ranking] == rank_files(ranking), "engine order violates documented tie rule" + assert {p for p, _ in ranking} <= universe, "engine ranked a path outside the snapshot" + packed = [sf.path for sf in res.selected_files if sf.path in universe] + return ranking, packed, res, latency + + +def evaluate_snapshot(key, group, manifest): + from agent_retrieval_bench.baseline import ( + read_jsonl, target_gold_files, query_text_for_eval, unique_ranked_paths, + rank_chunks_bm25_with_scores, recall_at, reciprocal_rank, tokenize, chunk_text, BM25_K1, BM25_B, + ) + from run_arb_evaluator_audit import reconstruct_file + from reponeedle.engine import RepoNeedleEngine + from reponeedle.models import FileDoc, TaskType + + chunks = read_jsonl(manifest[key]) + p2c = defaultdict(list) + for c in chunks: + if c.get("path"): + p2c[c["path"]].append(c) + universe = set(p2c) + corpus_order = list(p2c) + fragment, wholefile = {}, {} + for c in chunks: + p, t = c.get("path"), c.get("text", "") + if p and (p not in fragment or len(t) > len(fragment[p])): + fragment[p] = t + assert max((len(t) for t in fragment.values()), default=0) <= 25000, "fragment truncation would bind" + for p, cs in p2c.items(): + wholefile[p] = reconstruct_file(cs) + + def make_engine(texts, cut): + return RepoNeedleEngine.from_documents( + [FileDoc(path=p, content=(t[:25000] if cut else t), is_test="test" in p.lower()) for p, t in texts.items()]) + + eng_frag, eng_whole = make_engine(fragment, True), make_engine(wholefile, False) + bm_chunks = PrecomputedBM25(chunks, tokenize, chunk_text, BM25_K1, BM25_B) + bm_frag = PrecomputedBM25([{"path": p, "chunk_id": p, "text": t} for p, t in fragment.items()], tokenize, chunk_text, BM25_K1, BM25_B) + bm_whole = PrecomputedBM25([{"path": p, "chunk_id": p, "text": t} for p, t in wholefile.items()], tokenize, chunk_text, BM25_K1, BM25_B) + enum = {"code2test": TaskType.CODE2TEST, "trace2code": TaskType.TRACE2CODE, "edit2ripple": TaskType.EDIT2RIPPLE} + first = True + + for s in group: + gold = target_gold_files(s) + if not gold: + continue + task = s.get("task_type", s["_subset_name"]) + q = query_text_for_eval(s) + t_enum = enum.get(task, TaskType.COMMENT2CONTEXT) + + rank_frag, packed_frag, res_frag, lat_frag = _engine_ranking(eng_frag, q, t_enum, universe) + rank_whole, packed_whole, res_whole, lat_whole = _engine_ranking(eng_whole, q, t_enum, universe) + + timings = {} + bm25_paths = {} + for name, idx in [("bm25_chunks", bm_chunks), ("bm25_fragment", bm_frag), ("bm25_wholefile", bm_whole)]: + t0 = time.perf_counter() + ranked = idx.rank(q) + timings[name] = (time.perf_counter() - t0) * 1000 + bm25_paths[name] = unique_ranked_paths([c for _, c in ranked]) + if first: # exactness of the hoisted BM25 against ARB's own implementation + ref_chunks = idx.chunks + ref = rank_chunks_bm25_with_scores(q, ref_chunks) + assert [(round(a, 12), str(c.get("chunk_id"))) for a, c in ref] == \ + [(round(a, 12), str(c.get("chunk_id"))) for a, c in ranked], f"BM25 mismatch: {name}" + first = False + + rankings = { + "rn_fragment": [p for p, _ in rank_frag], + "rn_wholefile": [p for p, _ in rank_whole], + **bm25_paths, + } + for name, paths in rankings.items(): + assert len(paths) == len(set(paths)) and set(paths) <= universe, f"{name}: invalid ranking" + + metrics, independent_ok = {}, True + for name, paths in rankings.items(): + m = file_metrics(gold, paths) + arb = {"mrr": reciprocal_rank(set(gold), paths), **{f"recall@{k}": recall_at(set(gold), paths, k) for k in KS}} + independent_ok &= all(abs(m[k] - arb[k]) < 1e-15 for k in METRICS) + metrics[name] = arb + + official_seq = packed_frag + [p for p in corpus_order if p not in set(packed_frag)] + + def packed_stats(packed, res): + gs, ps = set(gold), set(packed) + return {"n_files": len(packed), "tokens": int(res.total_tokens), "utilisation": res.total_tokens / BUDGET, + "gold_coverage": len(gs & ps) / len(gs), "success": bool(gs & ps), + "gold_packed": sorted(gs & ps), "unselected_files": len(universe) - len(ps)} + + record = { + "sample_id": s["id"], "task_type": task, "repo": key[0], "base_commit": key[1], + "n_candidate_files": len(universe), "gold_files": gold, + "metrics": metrics, + "reciprocal_rank": {n: metrics[n]["mrr"] for n in rankings}, + "gold_positions": {n: gold_positions(gold, p) for n, p in rankings.items()}, + "packed": {"fragment": packed_stats(packed_frag, res_frag), "wholefile": packed_stats(packed_whole, res_whole)}, + "official_sequence_fidelity_only": {"mrr": reciprocal_rank(set(gold), official_seq), + **{f"recall@{k}": recall_at(set(gold), official_seq, k) for k in KS}}, + "latency_ms": {"rn_fragment_retrieve": lat_frag, "rn_wholefile_retrieve": lat_whole, **timings}, + "independent_metrics_agree": independent_ok, + "ranking_hash": {n: hashlib.sha256("\n".join(p).encode()).hexdigest()[:16] for n, p in rankings.items()}, + } + ranking_record = {"sample_id": s["id"], "top": {n: p[:TOP_N_STORED] for n, p in rankings.items()}, + "rn_scores_top": {"rn_fragment": [round(x, 6) for _, x in rank_frag[:TOP_N_STORED]], + "rn_wholefile": [round(x, 6) for _, x in rank_whole[:TOP_N_STORED]]}} + yield record, ranking_record + + +def run(shard, num_shards, keys_filter=None, out_dir=OUT_DIR): + manifest, by_corpus = _load() + (out_dir / "per_query").mkdir(parents=True, exist_ok=True) + (out_dir / "rankings").mkdir(parents=True, exist_ok=True) + if not (out_dir / "config.json").exists(): + (out_dir / "config.json").write_text(json.dumps(CONFIG, indent=2), encoding="utf-8") + keys = sorted(by_corpus) if keys_filter is None else keys_filter + mine = [k for i, k in enumerate(keys) if i % num_shards == shard] + pq = out_dir / "per_query" / f"shard_{shard}_of_{num_shards}.jsonl" + rk = out_dir / "rankings" / f"shard_{shard}_of_{num_shards}.jsonl" + done = {r["sample_id"] for r in _jsonl(pq)} if pq.exists() else set() + t0 = time.time() + with open(pq, "a", encoding="utf-8") as fq, open(rk, "a", encoding="utf-8") as fr: + for n, key in enumerate(mine, start=1): + group = [s for s in by_corpus[key] if s["id"] not in done] + if not group or not manifest.get(key) or not manifest[key].is_file(): + continue + for rec, rrec in evaluate_snapshot(key, group, manifest): + fq.write(json.dumps(rec) + "\n") + fr.write(json.dumps(rrec) + "\n") + fq.flush(); fr.flush() + print(f"[phase1 {shard}/{num_shards}] {n}/{len(mine)} snapshots, {time.time() - t0:.0f}s", file=sys.stderr, flush=True) + + +def check_determinism(n_snapshots): + """Re-run the first n snapshots into a scratch directory and compare ranking hashes.""" + import tempfile + manifest, by_corpus = _load() + keys = sorted(by_corpus)[:n_snapshots] + tmp = Path(tempfile.mkdtemp(prefix="phase1_det_")) + run(0, 1, keys_filter=keys, out_dir=tmp) + new = {r["sample_id"]: r for r in _jsonl(tmp / "per_query" / "shard_0_of_1.jsonl")} + old = {} + for p in (OUT_DIR / "per_query").glob("shard_*.jsonl"): + for r in _jsonl(p): + old[r["sample_id"]] = r + same = sum(new[i]["ranking_hash"] == old[i]["ranking_hash"] and new[i]["metrics"] == old[i]["metrics"] + for i in new if i in old) + result = {"snapshots": len(keys), "queries": len(new), "compared": sum(i in old for i in new), "identical": same} + (OUT_DIR / "determinism_check.json").write_text(json.dumps(result, indent=2), encoding="utf-8") + return result + + +def merge(): + rows = {} + dupes = 0 + for p in sorted((OUT_DIR / "per_query").glob("shard_*.jsonl")): + for r in _jsonl(p): + dupes += r["sample_id"] in rows + rows[r["sample_id"]] = r + from agent_retrieval_bench.baseline import read_jsonl, target_gold_files + expected = {} + for name in SUBSETS: + for s in read_jsonl(ARB_DIR / "data" / "benchmark" / f"v2_{name}" / "samples.jsonl"): + if target_gold_files(s): + expected[s["id"]] = (name, s["repo"]) + ids = sorted(rows) + + official_ref = {r["sample_id"]: r for p in CURRENT_DIR.glob("shard_*.jsonl") for r in _jsonl(p)} + hist = {r["sample_id"]: r for r in _jsonl(HIST_DIR / "checkpoint_positive.jsonl")} + p0_frag = {r["sample_id"]: r for p in PHASE0_DIR.glob("shard_*.jsonl") for r in _jsonl(p)} + p0_whole = {r["sample_id"]: r for p in list((PHASE0_DIR / "reconstructed_docs").glob("shard_*.jsonl")) + + list(PHASE0_DIR.glob("c2c_fast_*/reconstructed_docs/traces.jsonl")) for r in _jsonl(p)} + eq = lambda a, b: all(abs(a[k] - b[k]) < 1e-12 for k in METRICS) + fidelity = { + "queries_expected": len(expected), "queries_present": len(ids), + "missing": sorted(set(expected) - set(rows)), "unexpected": sorted(set(rows) - set(expected)), + "duplicate_rows": dupes, + "missing_gold_labels": sum(1 for i in ids if not rows[i]["gold_files"]), + "task_counts": dict(Counter(rows[i]["task_type"] for i in ids)), + "task_and_repo_match_dataset": sum(1 for i in ids if expected.get(i) == (rows[i]["task_type"], rows[i]["repo"])), + "independent_metric_agreement": sum(1 for i in ids if rows[i]["independent_metrics_agree"]), + "official_sequence_vs_official_current_engine_run": sum( + 1 for i in ids if i in official_ref and eq(rows[i]["official_sequence_fidelity_only"], official_ref[i])), + "bm25_chunks_vs_historical_official_bm25": sum( + 1 for i in ids if i in hist and eq(rows[i]["metrics"]["bm25_chunks"], hist[i]["bm25"])), + "rn_fragment_vs_phase0_audit": f"{sum(1 for i in ids if i in p0_frag and eq(rows[i]['metrics']['rn_fragment'], p0_frag[i]['rn_full']))}/{sum(1 for i in ids if i in p0_frag)}", + "rn_wholefile_vs_phase0_audit": f"{sum(1 for i in ids if i in p0_whole and eq(rows[i]['metrics']['rn_wholefile'], p0_whole[i]['rn_full']))}/{sum(1 for i in ids if i in p0_whole)}", + "bm25_wholefile_vs_phase0_audit": f"{sum(1 for i in ids if i in p0_whole and eq(rows[i]['metrics']['bm25_wholefile'], p0_whole[i]['bm25_file_reconstructed']))}/{sum(1 for i in ids if i in p0_whole)}", + "bm25_fragment_vs_phase0_audit": f"{sum(1 for i in ids if i in p0_whole and eq(rows[i]['metrics']['bm25_fragment'], p0_whole[i]['bm25_file_longest_chunk']))}/{sum(1 for i in ids if i in p0_whole)}", + } + + def agg(sel): + out = {"n_queries": len(sel), "n_repositories": len({rows[i]["repo"] for i in sel})} + for s in SYSTEMS: + out[s] = {k: float(np.mean([rows[i]["metrics"][s][k] for i in sel])) for k in METRICS} + for rep in ["fragment", "wholefile"]: + out[f"packed_{rep}"] = {k: float(np.mean([float(rows[i]["packed"][rep][k]) for i in sel])) + for k in ["n_files", "utilisation", "gold_coverage", "success", "unselected_files"]} + lat = lambda k: [rows[i]["latency_ms"][k] for i in sel] + out["latency_ms"] = {k: {"mean": float(np.mean(lat(k))), "p50": float(np.median(lat(k)))} + for k in ["rn_fragment_retrieve", "rn_wholefile_retrieve", "bm25_chunks", "bm25_fragment", "bm25_wholefile"]} + return out + + def cis(sel): + repos = [rows[i]["repo"] for i in sel] + res = [] + for a, b, label in COMPARISONS: + for k in METRICS: + d = np.array([rows[i]["metrics"][a][k] - rows[i]["metrics"][b][k] for i in sel]) + ci = clustered_bootstrap_ci(d, repos) + res.append({"a": a, "b": b, "label": label, "metric": k, "mean_diff": float(d.mean()), + "wins": int((d > 0).sum()), "ties": int((d == 0).sum()), "losses": int((d < 0).sum()), + "ci95": ci, "excludes_zero": bool(ci[0] > 0 or ci[1] < 0)}) + return res + + groups = {"overall": ids, **{t: [i for i in ids if rows[i]["task_type"] == t] for t in SUBSETS}} + aggregate = {g: agg(sel) for g, sel in groups.items()} + intervals = {g: cis(sel) for g, sel in groups.items()} + (OUT_DIR / "fidelity.json").write_text(json.dumps(fidelity, indent=2), encoding="utf-8") + (OUT_DIR / "aggregate.json").write_text(json.dumps(aggregate, indent=2), encoding="utf-8") + (OUT_DIR / "confidence_intervals.json").write_text(json.dumps(intervals, indent=2), encoding="utf-8") + meta = { + "git_head": subprocess.run(["git", "rev-parse", "HEAD"], cwd=ROOT_DIR, capture_output=True, text=True).stdout.strip(), + "working_tree_dirty": bool(subprocess.run(["git", "status", "--porcelain"], cwd=ROOT_DIR, capture_output=True, text=True).stdout.strip()), + "python": sys.version.split()[0], "numpy": np.__version__, "platform": platform.platform(), + "merged_at": time.strftime("%Y-%m-%dT%H:%M:%S"), + } + try: + import reponeedle + meta["reponeedle_version"] = reponeedle.__version__ + except Exception: + pass + (OUT_DIR / "run_metadata.json").write_text(json.dumps(meta, indent=2), encoding="utf-8") + return fidelity, aggregate, intervals + + +def main(argv=None): + ap = argparse.ArgumentParser(description="Supplemental product-aligned ARB v2 evaluation (not official).") + ap.add_argument("--shard", type=int, default=0) + ap.add_argument("--num-shards", type=int, default=1) + ap.add_argument("--merge", action="store_true") + ap.add_argument("--check-determinism", type=int, default=0, metavar="N_SNAPSHOTS") + args = ap.parse_args(argv) + if not (ARB_DIR / "src").exists(): + print(f"Missing ARB checkout at {ARB_DIR}", file=sys.stderr) + return 2 + if args.merge: + fidelity, _, _ = merge() + print(json.dumps(fidelity, indent=2)) + elif args.check_determinism: + print(json.dumps(check_determinism(args.check_determinism), indent=2)) + else: + run(args.shard, args.num_shards) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/benchmarks/run_complete_arb_evaluation.py b/benchmarks/run_complete_arb_evaluation.py index 9f075c3..17ee544 100644 --- a/benchmarks/run_complete_arb_evaluation.py +++ b/benchmarks/run_complete_arb_evaluation.py @@ -67,7 +67,7 @@ ) from agent_retrieval_bench.repomap_eval import build_repomap_index, rank_files_for_sample from agent_retrieval_bench.bcy_curve import count_tokens, pack_files, CorpusFileCache, load_corpus_manifest -from reponeedle.models import FileDoc, TaskType +from reponeedle.models import FileDoc, TaskType, BudgetConfig from reponeedle.engine import RepoNeedleEngine @@ -494,7 +494,12 @@ def run_full_evaluation(): else: docs = [FileDoc(path=f"{repo}/src/main.py", content=f"# {repo} main module")] - engine = RepoNeedleEngine.from_documents(docs) + # Abstention policy (1.2.0+): this phase measures the experimental abstention heuristic, so + # it is enabled explicitly (default threshold 0.12, unchanged). Phase 1 (positive retrieval) + # uses the 1.2 default (disabled). See docs/BENCHMARK_CONFIGURATION.md. + engine = RepoNeedleEngine.from_documents( + docs, budget_config=BudgetConfig(enable_experimental_abstention=True) + ) for s in needed_sel: sample_id = s["id"] diff --git a/benchmarks/run_doc_filter_validation.py b/benchmarks/run_doc_filter_validation.py new file mode 100644 index 0000000..6a88f4d --- /dev/null +++ b/benchmarks/run_doc_filter_validation.py @@ -0,0 +1,437 @@ +""" +Milestone 2B: held-out repository validation of the EXISTING documentation filter +(`AblationConfig.enable_doc_filter` / `doc_filter_penalty`). EXPERIMENTAL; not an official ARB score. + +Pre-registered workflow (each step refuses to run out of order): + 1. split -> split.json, split.sha256, protocol.json (dataset metadata only: repository x task coverage) + 2. dev -> filter OFF + predeclared penalty grid on DEVELOPMENT repositories only; selects and + freezes selected_filter_config.json (+ sha256) with the predeclared dev rule + 3. test -> verifies both hashes, runs OFF vs selected config ONCE on TEST repositories only + 4. report -> aggregates, CIs, Holm-adjusted guardrails, decision + +Evaluation framework: the Phase 1 product-aligned evaluation (file unit, whole-file input, full +ranking; packed context reported separately). Production code is not modified; the filter is applied +per call via `retrieve(ablation_config=...)`, and both arms pass an explicit AblationConfig. + +Usage: + python benchmarks/run_doc_filter_validation.py split + python benchmarks/run_doc_filter_validation.py dev --shard 0 --num-shards 4 (x4) + python benchmarks/run_doc_filter_validation.py select + python benchmarks/run_doc_filter_validation.py test --shard 0 --num-shards 4 (x4) + python benchmarks/run_doc_filter_validation.py report +""" + +import argparse +import hashlib +import json +import platform +import random +import subprocess +import sys +import time +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np + +ROOT_DIR = Path(__file__).resolve().parent.parent +OUT_DIR = ROOT_DIR / "reports" / "experimental" / "phase2_documentation_filter" +PHASE1_DIR = ROOT_DIR / "reports" / "experimental" / "phase1_product_aligned_evaluation" +sys.path.insert(0, str(Path(__file__).resolve().parent)) +sys.path.insert(0, str(ROOT_DIR)) + +import run_arb_product_aligned_eval as P1 # noqa: E402 (shared loaders, metrics, bootstrap) + +TASKS = ["comment2context", "code2test", "trace2code", "edit2ripple"] +METRICS = ["mrr", "recall@5", "recall@10", "recall@20"] + +PROTOCOL = { + "milestone": "2B documentation-filter held-out validation (EXPERIMENTAL; not an official ARB score)", + "framework": "Phase 1 product-aligned evaluation: file unit, whole-file input, full ranking; packed context separate", + "treatment": "AblationConfig(enable_doc_filter=True, doc_filter_penalty=p) passed per call to retrieve()", + "control": "AblationConfig() (enable_doc_filter=False) passed per call to retrieve()", + "doc_predicate": "engine.py legacy ranker: path contains /doc/, /docs/, doc/, docs/, documentation/, guide/, tutorial/, .github/ " + "OR ends with .md, .rst, .txt, .adoc, .html (case-insensitive)", + "split": { + "unit": "repository", "test_repositories": 10, "dev_repositories": 15, + "seed": "reponeedle-2B-split-v1", + "algorithm": "For attempt a = 0, 1, ...: shuffle the sorted repository list with random.Random(f'{seed}:{a}'); test = first 10. " + "Accept the first attempt where, for every task with n repositories, the test count is within +-1 of round(0.4*n) " + "and both splits contain >= 1 repository with that task.", + "inputs_used": "repository ids and task coverage (presence of >=1 gold-labelled query per task) only", + }, + "primary": {"task": "comment2context", "metric": "recall@20", "contrast": "filter ON - filter OFF on TEST repositories", + "success": "paired repository-clustered 95% percentile CI lower bound > 0"}, + "guardrails": { + "hypotheses": [["comment2context", "mrr"], ["code2test", "mrr"], ["code2test", "recall@20"], + ["trace2code", "mrr"], ["trace2code", "recall@20"], ["edit2ripple", "mrr"], ["edit2ripple", "recall@20"]], + "test": "one-sided regression test: bootstrap p = (1 + #{resampled mean diff >= 0}) / (B + 1)", + "correction": "Holm-Bonferroni over the 7 guardrail hypotheses, family-wise alpha = 0.05", + "note": "Recall@5 and Recall@10 for every task are reported with uncorrected CIs (descriptive)", + }, + "bootstrap": {"type": "paired, repository-clustered percentile", "resamples": P1.BOOT_N, "seed": P1.BOOT_SEED, "ci": 0.95}, + "dev_grid": [0.5, 0.25, 0.0], + "dev_selection_rule": "Among grid values whose DEV mean (ON - OFF) for comment2context MRR and for MRR and Recall@20 of every " + "other task is >= -0.02, choose the one maximising DEV mean comment2context Recall@20 gain, provided it is > 0; " + "ties -> larger penalty (milder). If none qualifies, freeze the existing default penalty 0.5 and record the dev failure.", + "decision_rule": { + "VALIDATED": "primary CI lower bound > 0 AND no Holm-supported guardrail regression", + "NOT VALIDATED": "any Holm-supported guardrail regression, OR primary point estimate <= 0", + "INCONCLUSIVE": "no Holm-supported regression, primary point estimate > 0, but primary CI includes 0; " + "or fewer than 5 test repositories contain comment2context queries", + }, + "budget_tokens": P1.BUDGET, + "task_label_passed_to_engine": True, +} + + +def canonical_hash(obj) -> str: + return hashlib.sha256(json.dumps(obj, sort_keys=True, separators=(",", ":")).encode()).hexdigest() + + +def is_doc(path: str) -> bool: + """Exact replica of the engine's documentation predicate (engine.py, legacy ranker).""" + p = path.lower() + return any(seg in p for seg in ["/doc/", "/docs/", "doc/", "docs/", "documentation/", "guide/", "tutorial/", ".github/"]) \ + or p.endswith((".md", ".rst", ".txt", ".adoc", ".html")) + + +def holm(pvalues): + """Holm-Bonferroni step-down adjusted p-values (monotone, capped at 1).""" + order = sorted(range(len(pvalues)), key=lambda i: pvalues[i]) + m, adj, running = len(pvalues), [0.0] * len(pvalues), 0.0 + for rank, i in enumerate(order): + running = max(running, min(1.0, (m - rank) * pvalues[i])) + adj[i] = running + return adj + + +def bootstrap(diffs, clusters, n_boot=P1.BOOT_N, seed=P1.BOOT_SEED): + """Returns (two-sided 95% percentile CI, one-sided regression p-value).""" + rng = np.random.default_rng(seed) + by = defaultdict(list) + for d, c in zip(diffs, clusters): + by[c].append(d) + groups = [np.asarray(v, dtype=float) for _, v in sorted(by.items())] + boots = np.array([np.concatenate([groups[i] for i in rng.integers(0, len(groups), len(groups))]).mean() + for _ in range(n_boot)]) + ci = [float(np.percentile(boots, 2.5)), float(np.percentile(boots, 97.5))] + p_regression = float((1 + np.sum(boots >= 0)) / (n_boot + 1)) + return ci, p_regression + + +# --------------------------------------------------------------------------- +# Step 1: split (metadata only) +# --------------------------------------------------------------------------- + +def make_split(): + if (OUT_DIR / "split.json").exists(): + raise SystemExit("split.json already exists; the split is frozen and must not be regenerated.") + from agent_retrieval_bench.baseline import read_jsonl, target_gold_files + coverage = defaultdict(set) + counts = defaultdict(Counter) + for t in TASKS: + for s in read_jsonl(P1.ARB_DIR / "data" / "benchmark" / f"v2_{t}" / "samples.jsonl"): + if target_gold_files(s): + coverage[s["repo"]].add(t) + counts[s["repo"]][t] += 1 + repos = sorted(coverage) + n_task = {t: sum(t in coverage[r] for r in repos) for t in TASKS} + target = {t: round(0.4 * n_task[t]) for t in TASKS} + seed = PROTOCOL["split"]["seed"] + for attempt in range(10_000): + order = list(repos) + random.Random(f"{seed}:{attempt}").shuffle(order) + test = set(order[: PROTOCOL["split"]["test_repositories"]]) + ok = all(abs(sum(t in coverage[r] for r in test) - target[t]) <= 1 + and 1 <= sum(t in coverage[r] for r in test) < n_task[t] for t in TASKS) + if ok: + break + else: + raise SystemExit("No split satisfied the stratification constraints; stop for review.") + split = { + "seed": seed, "accepted_attempt": attempt, "algorithm": PROTOCOL["split"]["algorithm"], + "assignment": {r: ("test" if r in test else "dev") for r in repos}, + "task_coverage": {r: sorted(coverage[r]) for r in repos}, + "query_counts_not_used_for_selection": {r: dict(counts[r]) for r in repos}, + "repositories_per_task": {t: {"total": n_task[t], "target_test": target[t], + "test": sum(t in coverage[r] for r in test), + "dev": sum(t in coverage[r] for r in repos if r not in test)} for t in TASKS}, + "queries_per_task": {t: {"dev": sum(counts[r][t] for r in repos if r not in test), + "test": sum(counts[r][t] for r in test)} for t in TASKS}, + } + OUT_DIR.mkdir(parents=True, exist_ok=True) + (OUT_DIR / "split.json").write_text(json.dumps(split, indent=2, sort_keys=True), encoding="utf-8") + h = canonical_hash(split) + (OUT_DIR / "split.sha256").write_text(f"{h} split.json (canonical JSON: sort_keys, compact separators)\n", encoding="utf-8") + protocol = dict(PROTOCOL, split_sha256=h, registered_at=time.strftime("%Y-%m-%dT%H:%M:%S")) + (OUT_DIR / "protocol.json").write_text(json.dumps(protocol, indent=2), encoding="utf-8") + (OUT_DIR / "protocol.sha256").write_text(canonical_hash(protocol) + " protocol.json\n", encoding="utf-8") + return split, h + + +def load_split(verify=True): + split = json.loads((OUT_DIR / "split.json").read_text(encoding="utf-8")) + if verify: + recorded = (OUT_DIR / "split.sha256").read_text(encoding="utf-8").split()[0] + assert canonical_hash(split) == recorded, "split.json hash mismatch: split was modified" + protocol = json.loads((OUT_DIR / "protocol.json").read_text(encoding="utf-8")) + assert protocol["split_sha256"] == recorded, "protocol does not reference this split" + return split + + +# --------------------------------------------------------------------------- +# Evaluation of arms on a set of repositories +# --------------------------------------------------------------------------- + +def evaluate(phase, arms, shard, num_shards): + from agent_retrieval_bench.baseline import read_jsonl, target_gold_files, query_text_for_eval + from run_arb_evaluator_audit import reconstruct_file + from reponeedle.engine import RepoNeedleEngine + from reponeedle.models import FileDoc, TaskType, AblationConfig + + split = load_split() + allowed = {r for r, s in split["assignment"].items() if s == phase} + manifest, by_corpus = P1._load() + keys = sorted(k for k in by_corpus if k[0] in allowed) + mine = [k for i, k in enumerate(keys) if i % num_shards == shard] + out = OUT_DIR / phase / f"shard_{shard}_of_{num_shards}.jsonl" + out.parent.mkdir(parents=True, exist_ok=True) + done = {json.loads(l)["sample_id"] for l in open(out, encoding="utf-8")} if out.exists() else set() + enum = {"code2test": TaskType.CODE2TEST, "trace2code": TaskType.TRACE2CODE, "edit2ripple": TaskType.EDIT2RIPPLE} + configs = {name: (AblationConfig() if pen is None else AblationConfig(enable_doc_filter=True, doc_filter_penalty=pen)) + for name, pen in arms.items()} + t0 = time.time() + with open(out, "a", encoding="utf-8") as f: + for n, key in enumerate(mine, start=1): + assert key[0] in allowed, "repository leakage across splits" + group = [s for s in by_corpus[key] if s["id"] not in done and target_gold_files(s)] + if not group: + continue + chunks = read_jsonl(manifest[key]) + p2c = defaultdict(list) + for c in chunks: + if c.get("path"): + p2c[c["path"]].append(c) + universe = set(p2c) + engine = RepoNeedleEngine.from_documents( + [FileDoc(path=p, content=reconstruct_file(cs), is_test="test" in p.lower()) for p, cs in p2c.items()]) + for s in group: + gold = target_gold_files(s) + task = s.get("task_type", s["_subset_name"]) + q = query_text_for_eval(s) + rec = {"sample_id": s["id"], "task_type": task, "repo": key[0], "base_commit": key[1], "phase": phase, + "gold_files": gold, "gold_docs": [g for g in gold if is_doc(g)], + "n_candidate_files": len(universe), "n_doc_files": sum(is_doc(p) for p in universe), "arms": {}} + for name, cfg in configs.items(): + captured = {} + orig = engine.budget_optimizer.pack_context + + def capture(candidate_files, *a, **kw): + captured["r"] = [(d.path, float(sc)) for d, sc, _ in candidate_files] + return orig(candidate_files, *a, **kw) + + engine.budget_optimizer.pack_context = capture + try: + t_q = time.perf_counter() + res = engine.retrieve(query=q, budget_tokens=P1.BUDGET, task_type=enum.get(task, TaskType.COMMENT2CONTEXT), + ablation_config=cfg) + lat = (time.perf_counter() - t_q) * 1000 + finally: + engine.budget_optimizer.pack_context = orig + ranking = [p for p, _ in captured["r"]] + assert ranking == P1.rank_files(captured["r"]) and set(ranking) <= universe + packed = [sf.path for sf in res.selected_files if sf.path in universe] + gs, ps = set(gold), set(packed) + rec["arms"][name] = { + "metrics": P1.file_metrics(gold, ranking), + "gold_positions": P1.gold_positions(gold, ranking), + "docs_top": {k: sum(is_doc(p) for p in ranking[:k]) for k in (5, 10, 20)}, + "packed": {"gold_coverage": len(gs & ps) / len(gs), "success": bool(gs & ps), + "utilisation": res.total_tokens / P1.BUDGET, "n_files": len(packed), + "n_doc_files": sum(is_doc(p) for p in packed), "gold_packed": sorted(gs & ps)}, + "latency_ms": lat, + "top20": ranking[:20], + "ranking_sha": hashlib.sha256("\n".join(ranking).encode()).hexdigest()[:16], + } + f.write(json.dumps(rec) + "\n") + f.flush() + print(f"[{phase} {shard}/{num_shards}] {n}/{len(mine)} snapshots, {time.time() - t0:.0f}s", file=sys.stderr, flush=True) + + +def load_rows(phase): + rows = {} + for p in sorted((OUT_DIR / phase).glob("shard_*.jsonl")): + for l in open(p, encoding="utf-8"): + r = json.loads(l) + rows[r["sample_id"]] = r + return rows + + +def contrasts(rows, arm_on, arm_off="off"): + out = {} + for t in ["overall"] + TASKS: + sel = sorted(i for i, r in rows.items() if t == "overall" or r["task_type"] == t) + if not sel: + continue + repos = [rows[i]["repo"] for i in sel] + block = {"n_queries": len(sel), "n_repositories": len(set(repos)), "metrics": {}} + for m in METRICS: + on = np.array([rows[i]["arms"][arm_on]["metrics"][m] for i in sel]) + off = np.array([rows[i]["arms"][arm_off]["metrics"][m] for i in sel]) + d = on - off + ci, p_reg = bootstrap(d, repos) + block["metrics"][m] = {"off": float(off.mean()), "on": float(on.mean()), "mean_diff": float(d.mean()), + "wins": int((d > 0).sum()), "ties": int((d == 0).sum()), "losses": int((d < 0).sum()), + "ci95": ci, "p_regression_one_sided": p_reg} + for arm in (arm_off, arm_on): + a = [rows[i]["arms"][arm] for i in sel] + block[arm] = { + "docs_top5": float(np.mean([x["docs_top"]["5"] if "5" in x["docs_top"] else x["docs_top"][5] for x in a])), + "docs_top10": float(np.mean([x["docs_top"]["10"] if "10" in x["docs_top"] else x["docs_top"][10] for x in a])), + "docs_top20": float(np.mean([x["docs_top"]["20"] if "20" in x["docs_top"] else x["docs_top"][20] for x in a])), + "packed_gold_coverage": float(np.mean([x["packed"]["gold_coverage"] for x in a])), + "packed_success": float(np.mean([x["packed"]["success"] for x in a])), + "packed_utilisation": float(np.mean([x["packed"]["utilisation"] for x in a])), + "latency_ms_mean": float(np.mean([x["latency_ms"] for x in a])), + "latency_ms_p50": float(np.median([x["latency_ms"] for x in a])), + } + out[t] = block + return out + + +def gold_effects(rows, arm_on, arm_off="off"): + """Gold files that leave the top 20 or the packed context under the filter (false negatives).""" + examples, counts = [], Counter() + for i, r in sorted(rows.items()): + on, off = r["arms"][arm_on], r["arms"][arm_off] + for g in r["gold_files"]: + doc = is_doc(g) + counts[("gold_doc" if doc else "gold_nondoc", "total")] += 1 + lost_top20 = g in off["top20"] and g not in on["top20"] + lost_pack = g in off["packed"]["gold_packed"] and g not in on["packed"]["gold_packed"] + gained_top20 = g not in off["top20"] and g in on["top20"] + for flag, v in [("lost_top20", lost_top20), ("lost_packed", lost_pack), ("gained_top20", gained_top20)]: + if v: + counts[("gold_doc" if doc else "gold_nondoc", flag)] += 1 + if lost_top20 or lost_pack: + examples.append({"sample_id": i, "task": r["task_type"], "repo": r["repo"], "gold": g, "gold_is_doc": doc, + "rank_off": off["gold_positions"].get(g), "rank_on": on["gold_positions"].get(g), + "lost_top20": lost_top20, "lost_packed": lost_pack}) + return {f"{a}:{b}": v for (a, b), v in sorted(counts.items())}, examples + + +# --------------------------------------------------------------------------- +# Step 2b: dev selection (dev data only) +# --------------------------------------------------------------------------- + +def select(): + if (OUT_DIR / "selected_filter_config.json").exists(): + raise SystemExit("selected_filter_config.json already frozen.") + split = load_split() + rows = load_rows("dev") + dev_repos = {r for r, s in split["assignment"].items() if s == "dev"} + assert {r["repo"] for r in rows.values()} <= dev_repos, "non-dev repository in dev results" + results, eligible = {}, [] + for pen in PROTOCOL["dev_grid"]: + name = f"on_p{pen}" + c = contrasts(rows, name) + results[name] = c + guard = [c["comment2context"]["metrics"]["mrr"]["mean_diff"]] + \ + [c[t]["metrics"][m]["mean_diff"] for t in ["code2test", "trace2code", "edit2ripple"] if t in c for m in ("mrr", "recall@20")] + gain = c["comment2context"]["metrics"]["recall@20"]["mean_diff"] + if min(guard) >= -0.02 and gain > 0: + eligible.append((gain, pen)) + if eligible: + best_gain = max(g for g, _ in eligible) + chosen = max(p for g, p in eligible if abs(g - best_gain) < 1e-12) + status = "selected by dev rule" + else: + chosen, status = 0.5, "no grid value met the dev rule; existing default penalty 0.5 frozen (dev failure recorded)" + effects = {f"on_p{p}": gold_effects(rows, f"on_p{p}")[0] for p in PROTOCOL["dev_grid"]} + (OUT_DIR / "dev_results.json").write_text(json.dumps({"contrasts": results, "gold_effects": effects}, indent=2), encoding="utf-8") + cfg = {"enable_doc_filter": True, "doc_filter_penalty": chosen, "status": status, + "dev_eligible": [{"penalty": p, "dev_c2c_recall20_gain": g} for g, p in eligible], + "split_sha256": (OUT_DIR / "split.sha256").read_text(encoding="utf-8").split()[0], + "frozen_at": time.strftime("%Y-%m-%dT%H:%M:%S")} + (OUT_DIR / "selected_filter_config.json").write_text(json.dumps(cfg, indent=2), encoding="utf-8") + (OUT_DIR / "selected_filter_config.sha256").write_text(canonical_hash(cfg) + " selected_filter_config.json\n", encoding="utf-8") + return cfg, results + + +def frozen_penalty(): + cfg = json.loads((OUT_DIR / "selected_filter_config.json").read_text(encoding="utf-8")) + recorded = (OUT_DIR / "selected_filter_config.sha256").read_text(encoding="utf-8").split()[0] + assert canonical_hash(cfg) == recorded, "selected config hash mismatch" + assert cfg["split_sha256"] == (OUT_DIR / "split.sha256").read_text(encoding="utf-8").split()[0] + return cfg["doc_filter_penalty"] + + +# --------------------------------------------------------------------------- +# Step 4: confirmatory report (test data) +# --------------------------------------------------------------------------- + +def report(): + split = load_split() + pen = frozen_penalty() + rows = load_rows("test") + test_repos = {r for r, s in split["assignment"].items() if s == "test"} + assert {r["repo"] for r in rows.values()} <= test_repos, "non-test repository in test results" + arm = "on_selected" + c = contrasts(rows, arm) + hyps = PROTOCOL["guardrails"]["hypotheses"] + pvals = [c[t]["metrics"][m]["p_regression_one_sided"] for t, m in hyps] + adj = holm(pvals) + guard = [{"task": t, "metric": m, "mean_diff": c[t]["metrics"][m]["mean_diff"], "ci95": c[t]["metrics"][m]["ci95"], + "p_one_sided": p, "p_holm": a, "regression_supported": a < 0.05} for (t, m), p, a in zip(hyps, pvals, adj)] + prim = c["comment2context"]["metrics"]["recall@20"] + n_c2c_repos = c["comment2context"]["n_repositories"] + if any(g["regression_supported"] for g in guard) or prim["mean_diff"] <= 0: + decision = "NOT VALIDATED" + elif prim["ci95"][0] > 0 and n_c2c_repos >= 5: + decision = "VALIDATED" + else: + decision = "INCONCLUSIVE" + effects, examples = gold_effects(rows, arm) + result = {"decision": decision, "frozen_penalty": pen, "primary": prim, "guardrails": guard, "contrasts": c, + "gold_effects": effects, "n_test_queries": len(rows), "n_test_repositories": len({r['repo'] for r in rows.values()})} + (OUT_DIR / "test_results.json").write_text(json.dumps(result, indent=2), encoding="utf-8") + (OUT_DIR / "false_negative_examples.json").write_text(json.dumps(examples, indent=2), encoding="utf-8") + meta = {"git_head": subprocess.run(["git", "rev-parse", "HEAD"], cwd=ROOT_DIR, capture_output=True, text=True).stdout.strip(), + "python": sys.version.split()[0], "numpy": np.__version__, "platform": platform.platform(), + "reported_at": time.strftime("%Y-%m-%dT%H:%M:%S")} + (OUT_DIR / "run_metadata.json").write_text(json.dumps(meta, indent=2), encoding="utf-8") + return result + + +def main(argv=None): + ap = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + ap.add_argument("step", choices=["split", "dev", "select", "test", "report"]) + ap.add_argument("--shard", type=int, default=0) + ap.add_argument("--num-shards", type=int, default=1) + args = ap.parse_args(argv) + if args.step == "split": + split, h = make_split() + print(json.dumps({"sha256": h, "accepted_attempt": split["accepted_attempt"], + "repositories_per_task": split["repositories_per_task"], + "queries_per_task": split["queries_per_task"]}, indent=2)) + elif args.step == "dev": + if (OUT_DIR / "selected_filter_config.json").exists(): + raise SystemExit("Configuration already frozen; dev evaluation is closed.") + arms = {"off": None, **{f"on_p{p}": p for p in PROTOCOL["dev_grid"]}} + evaluate("dev", arms, args.shard, args.num_shards) + elif args.step == "select": + cfg, _ = select() + print(json.dumps(cfg, indent=2)) + elif args.step == "test": + pen = frozen_penalty() + evaluate("test", {"off": None, "on_selected": pen}, args.shard, args.num_shards) + elif args.step == "report": + r = report() + print(json.dumps({"decision": r["decision"], "penalty": r["frozen_penalty"], "primary": r["primary"], + "guardrails": r["guardrails"]}, indent=2)) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/benchmarks/run_lexical_forensics.py b/benchmarks/run_lexical_forensics.py new file mode 100644 index 0000000..f009453 --- /dev/null +++ b/benchmarks/run_lexical_forensics.py @@ -0,0 +1,506 @@ +""" +Milestone 2C: lexical retrieval forensics and DEV-ONLY ablations. EXPERIMENTAL; not an official ARB score. + +Data: the 15 Milestone 2B DEVELOPMENT repositories only (previously used development data). The 10 +Milestone 2B held-out repositories are never loaded (asserted). No fresh confirmatory data exists +locally: every local ARB split uses the same 25 repositories. + +Framework: Phase 1 product-aligned evaluation (whole-file input reconstructed from ARB chunks, file +unit, full ranking, score desc / path asc ties, file-level MRR and Recall@K). + +Steps (each refuses to run out of order): + register -> protocol.json + sha256 (experiment matrix and selection rules), before any variant result + lexical -> stage 1: lexical-only rankings for every registered variant + forensic statistics + stage1 -> applies the registered stage-1 rule; writes stage1_results.json and hybrid_candidates.json + hybrid -> stage 2: full RepoNeedle hybrid ranking with the candidate lexical variants swapped in + stage2 -> applies the registered stage-2 rule; writes dev_selection.json + +Production code is not modified by this script: variants are harness subclasses of BM25Index. +""" + +import argparse +import hashlib +import json +import re +import sys +import time +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np + +ROOT_DIR = Path(__file__).resolve().parent.parent +OUT_DIR = ROOT_DIR / "reports" / "experimental" / "phase3_lexical_retrieval" +PHASE2_DIR = ROOT_DIR / "reports" / "experimental" / "phase2_documentation_filter" +sys.path.insert(0, str(Path(__file__).resolve().parent)) +sys.path.insert(0, str(ROOT_DIR)) + +import run_arb_product_aligned_eval as P1 # noqa: E402 +from reponeedle.core.lexical import BM25Index # noqa: E402 +from reponeedle.core.tokenizer import CodeTokenizer, WORD_REGEX, split_identifier # noqa: E402 + +TASKS = ["comment2context", "code2test", "trace2code", "edit2ripple"] +METRICS = ["mrr", "recall@5", "recall@10", "recall@20"] + +# Registered lexical variants: one factor changed relative to L0 (current production defaults). +VARIANTS = { + "L0_baseline": {}, + "L1_path_weight_1.0": {"path_weight": 1.0}, + "L2_b_0.4": {"b": 0.4}, + "L3_b_0.9": {"b": 0.9}, + "L4_query_term_frequency": {"query_tf": True}, + "L5_no_duplicate_tokens": {"dedupe": True}, + "L6_json_values_only": {"json_values_only": True}, +} +DIAGNOSTICS = { + "D_path_field_only": {"body_weight": 0.0}, + "D_body_field_only": {"path_weight": 0.0}, +} + +PROTOCOL = { + "milestone": "2C lexical retrieval forensics and dev-only optimisation (EXPERIMENTAL; not an official ARB score)", + "data": "Milestone 2B DEVELOPMENT repositories only (previously used development data); 2B held-out repositories excluded", + "phase2_split_sha256": None, # filled at registration + "framework": "Phase 1 product-aligned evaluation: whole-file input, file unit, full ranking", + "variants": VARIANTS, + "variant_semantics": { + "path_weight / b": "existing BM25Index constructor parameters", + "query_tf": "multiply each query term's BM25 contribution by its query count (ARB BM25 behaviour)", + "dedupe": "emit each token at most once per source word (current tokenizer emits plain words twice: raw form and identical piece)", + "json_values_only": "if the query parses as a JSON object, score only its leaf values (key names dropped); otherwise unchanged", + }, + "diagnostics": DIAGNOSTICS, + "stage1_rule": "Lexical-only, dev. A variant is a hybrid candidate if its dev mean (variant - L0) comment2context Recall@20 is > 0 " + "and its dev mean for MRR and Recall@20 of every task is >= -0.02. At most the 3 candidates with the largest " + "comment2context Recall@20 gain go to stage 2, plus 'C_combined' = all candidates applied together when >= 2 qualify.", + "stage2_rule": "Full hybrid, dev. Select the arm with the largest dev mean comment2context Recall@20 gain over the hybrid baseline " + "among arms whose dev mean MRR and Recall@20 for every task (and comment2context MRR) is >= -0.02. The selection is " + "'dev-supported' only if its repository-clustered 95% CI for comment2context Recall@20 excludes zero (positive). " + "If no arm is dev-supported, no default change is made; any implementation stays opt-in.", + "bootstrap": {"type": "paired, repository-clustered percentile", "resamples": P1.BOOT_N, "seed": P1.BOOT_SEED}, + "confirmatory_requirement": "Fresh repositories disjoint from all 25 ARB v2 repositories, independently labelled, covering all four " + "tasks, split at repository level with a recorded hash, protocol registered before treatment results. " + "If unavailable: no confirmatory claim (INCONCLUSIVE).", +} + + +def canonical_hash(obj) -> str: + return hashlib.sha256(json.dumps(obj, sort_keys=True, separators=(",", ":")).encode()).hexdigest() + + +# --------------------------------------------------------------------------- +# Variant lexical index (harness only) +# --------------------------------------------------------------------------- + +class DedupeTokenizer(CodeTokenizer): + """Same tokens as CodeTokenizer, but each token at most once per source word.""" + + def tokenize_text(self, text, include_raw_identifiers=True): + if not text: + return [] + out = [] + for word in WORD_REGEX.findall(text): + seen = set() + lw = word.lower() + if include_raw_identifiers and lw not in self.stop_words: + out.append(lw) + seen.add(lw) + for p in split_identifier(word): + if p not in self.stop_words and len(p) > 1 and p not in seen: + out.append(p) + seen.add(p) + return out + + +def json_leaf_text(query: str) -> str: + try: + obj = json.loads(query) + except (ValueError, TypeError): + return query + if not isinstance(obj, dict): + return query + leaves = [] + + def walk(x): + if isinstance(x, dict): + for v in x.values(): + walk(v) + elif isinstance(x, list): + for v in x: + walk(v) + elif x is not None: + leaves.append(str(x)) + walk(obj) + return "\n".join(leaves) + + +class VariantBM25(BM25Index): + def __init__(self, query_tf=False, dedupe=False, json_values_only=False, **kw): + super().__init__(**kw) + self.query_tf, self.json_values_only = query_tf, json_values_only + if dedupe: + self.tokenizer = DedupeTokenizer() + + def score(self, query): + if self.json_values_only: + query = json_leaf_text(query) + if not self.query_tf: + return super().score(query) + if self.num_docs == 0: + return np.array([], dtype=np.float32) + q_tokens = self.tokenizer.tokenize_text(query) + scores = np.zeros(self.num_docs, dtype=np.float32) + counts = Counter(q_tokens) + k1p = self.k1 + 1.0 + for term, q_cnt in counts.items(): + idf = self.idf.get(term, 0.0) + if idf <= 0.0: + continue + for doc_idx, tf in self.inverted_index.get(term, []): + scores[doc_idx] += q_cnt * idf * (tf * k1p) / (tf + self.doc_len_norms[doc_idx]) + m = float(scores.max()) if len(scores) else 0.0 + return scores / m if m > 0 else scores + + + def fit_tokens(self, docs, tokens): + """Exact replica of BM25Index.fit using pre-computed (path_tokens, symbol_tokens, body_tokens) per doc. + Equality with fit() is asserted at runtime on every snapshot.""" + self.docs, self.num_docs, self.doc_paths = docs, len(docs), [d.path for d in docs] + self.doc_frequencies, self.doc_term_freqs, lengths = {}, [], [] + for doc in docs: + path_tokens, symbol_tokens, body_tokens = tokens[doc.path] + wc = {} + for t in path_tokens: + wc[t] = wc.get(t, 0.0) + self.path_weight + for t in symbol_tokens: + wc[t] = wc.get(t, 0.0) + self.symbol_weight + for t in body_tokens: + wc[t] = wc.get(t, 0.0) + self.body_weight + eff = max(1.0, float(len(body_tokens) + len(path_tokens))) if self.decouple_symbols else sum(wc.values()) + lengths.append(eff) + self.doc_term_freqs.append(wc) + for term in set(wc.keys()): + self.doc_frequencies[term] = self.doc_frequencies.get(term, 0) + 1 + self.doc_lengths = np.array(lengths, dtype=np.float32) + self.avg_doc_len = float(np.mean(self.doc_lengths)) if len(self.doc_lengths) > 0 else 1.0 + inv = defaultdict(list) + for i, tc in enumerate(self.doc_term_freqs): + for term, tf in tc.items(): + inv[term].append((i, tf)) + self.inverted_index = dict(inv) + self.doc_len_norms = (self.k1 * (1.0 - self.b + self.b * (self.doc_lengths / self.avg_doc_len)) + if self.avg_doc_len > 0 else np.ones(self.num_docs, dtype=np.float32) * self.k1) + import math + self.idf = {t: max(0.0, math.log((self.num_docs - df + 0.5) / (df + 0.5) + 1.0)) for t, df in self.doc_frequencies.items()} + return self + + +def make_index(cfg): + return VariantBM25(**cfg) + + +def shared_tokens(docs, tokenizer): + return {d.path: (tokenizer.tokenize_path(d.path), + [t for s in d.symbols for t in tokenizer.tokenize_text(s)], + tokenizer.tokenize_text(d.content)) for d in docs} + + +# --------------------------------------------------------------------------- +# Data helpers +# --------------------------------------------------------------------------- + +def dev_repos(): + split = json.loads((PHASE2_DIR / "split.json").read_text(encoding="utf-8")) + recorded = (PHASE2_DIR / "split.sha256").read_text(encoding="utf-8").split()[0] + assert canonical_hash(split) == recorded, "Milestone 2B split hash mismatch" + return {r for r, s in split["assignment"].items() if s == "dev"}, recorded + + +def snapshot_docs(key, manifest): + from agent_retrieval_bench.baseline import read_jsonl + from run_arb_evaluator_audit import reconstruct_file + from reponeedle.models import FileDoc + p2c = defaultdict(list) + for c in read_jsonl(manifest[key]): + if c.get("path"): + p2c[c["path"]].append(c) + return [FileDoc(path=p, content=reconstruct_file(cs), is_test="test" in p.lower()) for p, cs in p2c.items()] + + +def rank_by(scores, paths): + return P1.rank_files(list(zip(paths, [float(s) for s in scores]))) + + +# --------------------------------------------------------------------------- +# Steps +# --------------------------------------------------------------------------- + +def register(): + if (OUT_DIR / "protocol.json").exists(): + raise SystemExit("protocol.json already registered.") + _, split_hash = dev_repos() + proto = dict(PROTOCOL, phase2_split_sha256=split_hash, registered_at=time.strftime("%Y-%m-%dT%H:%M:%S")) + OUT_DIR.mkdir(parents=True, exist_ok=True) + (OUT_DIR / "protocol.json").write_text(json.dumps(proto, indent=2), encoding="utf-8") + (OUT_DIR / "protocol.sha256").write_text(canonical_hash(proto) + " protocol.json\n", encoding="utf-8") + return proto + + +def verify_protocol(): + proto = json.loads((OUT_DIR / "protocol.json").read_text(encoding="utf-8")) + assert canonical_hash(proto) == (OUT_DIR / "protocol.sha256").read_text(encoding="utf-8").split()[0], "protocol modified" + return proto + + +def run_lexical(shard, num_shards): + from agent_retrieval_bench.baseline import target_gold_files, query_text_for_eval, tokenize as arb_tokenize + verify_protocol() + allowed, _ = dev_repos() + manifest, by = P1._load() + keys = sorted(k for k in by if k[0] in allowed) + mine = [k for i, k in enumerate(keys) if i % num_shards == shard] + out = OUT_DIR / "lexical" / f"shard_{shard}_of_{num_shards}.jsonl" + out.parent.mkdir(parents=True, exist_ok=True) + done = {json.loads(l)["sample_id"] for l in open(out, encoding="utf-8")} if out.exists() else set() + tok = CodeTokenizer() + t0 = time.time() + guard_checked = False + with open(out, "a", encoding="utf-8") as f: + for n, key in enumerate(mine, start=1): + assert key[0] in allowed, "held-out repository leaked into dev analysis" + group = [s for s in by[key] if s["id"] not in done and target_gold_files(s)] + if not group: + continue + docs = snapshot_docs(key, manifest) + paths = [d.path for d in docs] + toks = {False: shared_tokens(docs, CodeTokenizer()), True: shared_tokens(docs, DedupeTokenizer())} + indexes = {name: make_index(cfg).fit_tokens(docs, toks[bool(cfg.get("dedupe"))]) + for name, cfg in {**VARIANTS, **DIAGNOSTICS}.items()} + base = indexes["L0_baseline"] + # Exactness guard (first snapshot processed by each shard run): shared-token construction must + # equal the production fit() bit for bit, for the default and the de-duplicating tokenizer. + if not guard_checked: + probe = query_text_for_eval(group[0]) + assert np.array_equal(base.score(probe), BM25Index().fit(docs).score(probe)), "fit_tokens != BM25Index.fit" + assert np.array_equal(indexes["L5_no_duplicate_tokens"].score(probe), + make_index(VARIANTS["L5_no_duplicate_tokens"]).fit(docs).score(probe)), "dedupe fit mismatch" + guard_checked = True + print(f"[lexical {shard}/{num_shards}] exactness guard passed on {key[0]}", file=sys.stderr, flush=True) + eff_len = dict(zip(paths, base.doc_lengths.tolist())) + median_len = float(np.median(base.doc_lengths)) + for s in group: + gold = target_gold_files(s) + q = query_text_for_eval(s) + rec = {"sample_id": s["id"], "task_type": s.get("task_type", s["_subset_name"]), "repo": key[0], + "n_files": len(paths), "gold_files": gold, "variants": {}} + for name, idx in indexes.items(): + ranking = rank_by(idx.score(q), paths) + rec["variants"][name] = {"metrics": P1.file_metrics(gold, ranking), + "gold_positions": P1.gold_positions(gold, ranking), + "top10": ranking[:10]} + # Query tokenisation forensics + rn_tokens = tok.tokenize_text(q) + is_json = False + key_tokens = 0 + field_tokens = {} + try: + obj = json.loads(q) + if isinstance(obj, dict): + is_json = True + key_tokens = sum(len(tok.tokenize_text(k)) for k in obj) + field_tokens = {k: len(tok.tokenize_text(json.dumps(v) if not isinstance(v, str) else v)) for k, v in obj.items()} + except ValueError: + pass + counts = Counter(rn_tokens) + rec["query"] = { + "is_json": is_json, "chars": len(q), "rn_tokens": len(rn_tokens), "rn_unique": len(counts), + "arb_tokens": len(arb_tokenize(q)), "key_tokens": key_tokens, "field_tokens": field_tokens, + "numeric_token_share": sum(c for t, c in counts.items() if t.isdigit()) / max(1, len(rn_tokens)), + "max_term_repeat": max(counts.values()) if counts else 0, + } + # Length forensics (effective BM25 length, baseline index) + top10 = rec["variants"]["L0_baseline"]["top10"] + rec["length"] = { + "corpus_median": median_len, + "gold": [eff_len.get(g) for g in gold], + "gold_percentile": [float(np.mean(base.doc_lengths <= eff_len[g])) if g in eff_len else None for g in gold], + "top10_nongold_median": float(np.median([eff_len[p] for p in top10 if p not in set(gold)] or [0])), + } + f.write(json.dumps(rec) + "\n") + f.flush() + print(f"[lexical {shard}/{num_shards}] {n}/{len(mine)} snapshots, {time.time() - t0:.0f}s", file=sys.stderr, flush=True) + + +def load(stage): + rows = {} + for p in sorted((OUT_DIR / stage).glob("shard_*.jsonl")): + for l in open(p, encoding="utf-8"): + r = json.loads(l) + rows[r["sample_id"]] = r + return rows + + +def contrast(rows, a, b, key="variants"): + out = {} + for t in ["overall"] + TASKS: + sel = sorted(i for i, r in rows.items() if t == "overall" or r["task_type"] == t) + if not sel: + continue + repos = [rows[i]["repo"] for i in sel] + blk = {"n_queries": len(sel), "n_repositories": len(set(repos))} + for m in METRICS: + va = np.array([rows[i][key][a]["metrics"][m] for i in sel]) + vb = np.array([rows[i][key][b]["metrics"][m] for i in sel]) + d = va - vb + blk[m] = {"a": float(va.mean()), "b": float(vb.mean()), "mean_diff": float(d.mean()), + "wins": int((d > 0).sum()), "ties": int((d == 0).sum()), "losses": int((d < 0).sum()), + "ci95": P1.clustered_bootstrap_ci(d, repos)} + out[t] = blk + return out + + +def stage1(): + verify_protocol() + rows = load("lexical") + p1 = {} + for p in (P1.OUT_DIR / "per_query").glob("shard_*.jsonl"): + for l in open(p, encoding="utf-8"): + r = json.loads(l) + p1[r["sample_id"]] = r + results = {name: contrast(rows, name, "L0_baseline") for name in list(VARIANTS)[1:] + list(DIAGNOSTICS)} + # Absolute dev metrics for every variant and for the matched BM25 / hybrid references (Phase 1 rows) + absolute = {} + for t in ["overall"] + TASKS: + sel = [i for i, r in rows.items() if t == "overall" or r["task_type"] == t] + absolute[t] = {name: {m: float(np.mean([rows[i]["variants"][name]["metrics"][m] for i in sel])) for m in METRICS} + for name in {**VARIANTS, **DIAGNOSTICS}} + absolute[t]["REF_bm25_wholefile"] = {m: float(np.mean([p1[i]["metrics"]["bm25_wholefile"][m] for i in sel])) for m in METRICS} + absolute[t]["REF_hybrid_wholefile"] = {m: float(np.mean([p1[i]["metrics"]["rn_wholefile"][m] for i in sel])) for m in METRICS} + absolute[t]["n_queries"] = len(sel) + candidates = [] + for name in list(VARIANTS)[1:]: + c = results[name] + gain = c["comment2context"]["recall@20"]["mean_diff"] + guard = min(c[t][m]["mean_diff"] for t in TASKS if t in c for m in ("mrr", "recall@20")) + if gain > 0 and guard >= -0.02: + candidates.append((gain, name)) + candidates = [n for _, n in sorted(candidates, reverse=True)[:3]] + (OUT_DIR / "stage1_results.json").write_text(json.dumps({"contrasts_vs_L0": results, "absolute": absolute, + "n_queries": len(rows)}, indent=2), encoding="utf-8") + hc = {"candidates": candidates, "combined": len(candidates) >= 2, + "combined_config": {k: v for n in candidates for k, v in VARIANTS[n].items()} if len(candidates) >= 2 else None} + (OUT_DIR / "hybrid_candidates.json").write_text(json.dumps(hc, indent=2), encoding="utf-8") + (OUT_DIR / "hybrid_candidates.sha256").write_text(canonical_hash(hc) + " hybrid_candidates.json\n", encoding="utf-8") + return hc, absolute, results + + +def hybrid_arms(): + hc = json.loads((OUT_DIR / "hybrid_candidates.json").read_text(encoding="utf-8")) + assert canonical_hash(hc) == (OUT_DIR / "hybrid_candidates.sha256").read_text(encoding="utf-8").split()[0] + arms = {"H0_baseline": {}} + for n in hc["candidates"]: + arms[f"H_{n}"] = VARIANTS[n] + if hc["combined"]: + arms["H_C_combined"] = hc["combined_config"] + return arms + + +def run_hybrid(shard, num_shards): + from agent_retrieval_bench.baseline import target_gold_files, query_text_for_eval + from reponeedle.engine import RepoNeedleEngine + from reponeedle.models import TaskType, AblationConfig + verify_protocol() + arms = hybrid_arms() + allowed, _ = dev_repos() + manifest, by = P1._load() + keys = sorted(k for k in by if k[0] in allowed) + mine = [k for i, k in enumerate(keys) if i % num_shards == shard] + out = OUT_DIR / "hybrid" / f"shard_{shard}_of_{num_shards}.jsonl" + out.parent.mkdir(parents=True, exist_ok=True) + done = {json.loads(l)["sample_id"] for l in open(out, encoding="utf-8")} if out.exists() else set() + enum = {"code2test": TaskType.CODE2TEST, "trace2code": TaskType.TRACE2CODE, "edit2ripple": TaskType.EDIT2RIPPLE} + t0 = time.time() + with open(out, "a", encoding="utf-8") as f: + for n, key in enumerate(mine, start=1): + assert key[0] in allowed + group = [s for s in by[key] if s["id"] not in done and target_gold_files(s)] + if not group: + continue + docs = snapshot_docs(key, manifest) + engine = RepoNeedleEngine.from_documents(docs) + production_index = engine.lexical_index + arm_index = {a: (production_index if not cfg else make_index(cfg).fit(engine.docs)) for a, cfg in arms.items()} + universe = {d.path for d in docs} + for s in group: + gold = target_gold_files(s) + task = s.get("task_type", s["_subset_name"]) + rec = {"sample_id": s["id"], "task_type": task, "repo": key[0], "gold_files": gold, "arms": {}} + for a, idx in arm_index.items(): + engine.lexical_index = idx + cap = {} + orig = engine.budget_optimizer.pack_context + + def capture(candidate_files, *x, **kw): + cap["r"] = [(d.path, float(sc)) for d, sc, _ in candidate_files] + return orig(candidate_files, *x, **kw) + engine.budget_optimizer.pack_context = capture + try: + res = engine.retrieve(query=query_text_for_eval(s), budget_tokens=P1.BUDGET, + task_type=enum.get(task, TaskType.COMMENT2CONTEXT), ablation_config=AblationConfig()) + finally: + engine.budget_optimizer.pack_context = orig + ranking = [p for p, _ in cap["r"]] + packed = {sf.path for sf in res.selected_files if sf.path in universe} + rec["arms"][a] = {"metrics": P1.file_metrics(gold, ranking), "gold_positions": P1.gold_positions(gold, ranking), + "packed_gold_coverage": len(set(gold) & packed) / len(gold)} + engine.lexical_index = production_index + f.write(json.dumps(rec) + "\n") + f.flush() + print(f"[hybrid {shard}/{num_shards}] {n}/{len(mine)} snapshots, {time.time() - t0:.0f}s", file=sys.stderr, flush=True) + + +def stage2(): + verify_protocol() + rows = load("hybrid") + arms = [a for a in hybrid_arms() if a != "H0_baseline"] + results = {a: contrast(rows, a, "H0_baseline", key="arms") for a in arms} + eligible = [] + for a, c in results.items(): + guard = min(c[t][m]["mean_diff"] for t in TASKS if t in c for m in ("mrr", "recall@20")) + gain = c["comment2context"]["recall@20"]["mean_diff"] + if guard >= -0.02: + eligible.append((gain, a)) + sel = max(eligible)[1] if eligible else None + supported = bool(sel and results[sel]["comment2context"]["recall@20"]["ci95"][0] > 0) + decision = {"selected_arm": sel, "dev_supported": supported, "eligible": [{"arm": a, "c2c_recall20_gain": g} for g, a in eligible], + "selected_config": hybrid_arms().get(sel) if sel else None, + "note": "development evidence only (previously used development repositories); not confirmatory"} + (OUT_DIR / "dev_selection.json").write_text(json.dumps(decision, indent=2), encoding="utf-8") + (OUT_DIR / "dev_selection.sha256").write_text(canonical_hash(decision) + " dev_selection.json\n", encoding="utf-8") + (OUT_DIR / "stage2_results.json").write_text(json.dumps(results, indent=2), encoding="utf-8") + return decision, results + + +def main(argv=None): + ap = argparse.ArgumentParser() + ap.add_argument("step", choices=["register", "lexical", "stage1", "hybrid", "stage2"]) + ap.add_argument("--shard", type=int, default=0) + ap.add_argument("--num-shards", type=int, default=1) + a = ap.parse_args(argv) + if a.step == "register": + print(json.dumps({"protocol_sha256": canonical_hash(register())}, indent=2)) + elif a.step == "lexical": + run_lexical(a.shard, a.num_shards) + elif a.step == "stage1": + hc, _, _ = stage1() + print(json.dumps(hc, indent=2)) + elif a.step == "hybrid": + run_hybrid(a.shard, a.num_shards) + elif a.step == "stage2": + d, _ = stage2() + print(json.dumps(d, indent=2)) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/benchmarks/runner.py b/benchmarks/runner.py index f83773e..87025f9 100644 --- a/benchmarks/runner.py +++ b/benchmarks/runner.py @@ -93,13 +93,18 @@ def run_all(self, budgets: List[int] = [2000, 4000, 8000]) -> Dict[str, Any]: # 1. Ranking evaluation (using standard 4k budget) if sys_name == "RepoNeedle": + # Abstention policy (1.2.0+): the experimental heuristic is opt-in. It is enabled + # only for selective (no-gold) samples, which measure it (SelectiveAccuracy). + # Positive samples use the 1.2 default; verified to give identical positive + # metrics to the 1.1.0 always-on behavior (see docs/BENCHMARK_CONFIGURATION.md). res_4k = runner.retrieve( query=Query( text=sample.query, task_type=sample.task_type, anchor_files=sample.anchor_files, budget_tokens=4000, - ) + ), + enable_experimental_abstention=is_selective, ) else: res_4k = runner.retrieve(sample.query, budget_tokens=4000) diff --git a/docs/ANNOUNCEMENT.md b/docs/ANNOUNCEMENT.md index c6e3899..0af78ea 100644 --- a/docs/ANNOUNCEMENT.md +++ b/docs/ANNOUNCEMENT.md @@ -1,5 +1,13 @@ # 🪡 Announcing RepoNeedle: Token-Budget-Aware, Workflow-Adaptive Repository Context Retrieval for AI Coding Agents +> [!CAUTION] +> **Historical document with corrections (corrected 2026-11-09).** This announcement was written for the **v0.1.0 research preview** (October 2026). It is kept for the record, but several of its original claims were not supported by later evaluation and have been corrected in place below: +> - **Selective abstention**: The original text described a "calibrated" abstention mechanism with perfect accuracy. That figure came from a synthetic benchmark containing only **3** selective samples. On the official Agent Retrieval Bench v2 selective split (**N = 82** no-gold queries) the heuristic correctly abstained on **0 of 82 (0.0%)**. The heuristic is uncalibrated and experimental, and on the current development branch (planned for 1.2.0, unreleased) it is **disabled by default**. +> - **Benchmark table**: The table below reports the v0.1.0 synthetic **RepoNeedle-Bench** run (15 samples). The stored result file (`benchmarks/results/benchmark_results.json`) is **not reproducible** by any later code version (1.1.0 or the current branch); current re-run values are shown alongside. +> - **Latency**: Sub-millisecond latency was measured on tiny synthetic corpora. On real repositories (ARB v2) mean query latency is ~318 ms (see README, Part D). +> +> For current, evidence-backed results see [README.md](../README.md), [CLAIMS_AND_EVIDENCE.md](../CLAIMS_AND_EVIDENCE.md), [KNOWN_LIMITATIONS.md](../KNOWN_LIMITATIONS.md) and [BENCHMARK_CONFIGURATION.md](BENCHMARK_CONFIGURATION.md). + Today we are releasing **RepoNeedle**: an open-source, reproducible repository-context retrieval system engineered specifically for AI coding agents operating under strict context-token budgets. RepoNeedle is available under the permissive **MIT License**, with full reproducible benchmarks, dataset cards, and an interactive Playground on Hugging Face. @@ -23,30 +31,41 @@ When an agent tackles a bug report, adds regression tests, or refactors a compon RepoNeedle solves these bottlenecks through four complementary innovations: 1. **Workflow-Adaptive Routing**: Automatically classifies task intent (`trace2code`, `code2test`, `edit2ripple`, `comment2context`, `selective`) and dynamically adjusts retrieval weights between AST symbols, inverted dependency graphs, dense subwords, and lexical matching. -2. **Inverted Test-to-Target Mapping**: Connects production code to its corresponding test suites via import reversal and naming conventions, achieving a **1.000 MRR on test discovery** (where RepoMap scores 0.122). -3. **Calibrated Selective Abstention**: Uses peak-to-noise ratio ($Z$-score) and repository symbol overlap to abstain when a query is out-of-scope, saving **100% of context tokens** on counterfactual queries. -4. **Token-Budget Knapsack Optimization**: Uses marginal utility density packing with multi-tier structural skeletons (extracting signatures, docstrings, and query matches) to pack 3-4x more relevant files into fixed token budgets (2k, 4k, 8k tokens). +2. **Inverted Test-to-Target Mapping**: Connects production code to its corresponding test suites via import reversal and naming conventions, achieving a **1.000 MRR on test discovery** on the synthetic benchmark (where RepoMap scores 0.122). *On real repositories (ARB v2 `code2test`, N=106) the measured MRR is 0.2660 vs RepoMap 0.1942; see README.* +3. **Selective Abstention (experimental heuristic, corrected)**: A heuristic combining repository vocabulary overlap, score peakiness ($Z$-score) and score margin was intended to abstain on out-of-scope queries. *Correction: it is not calibrated. It correctly abstained on 0 of 82 no-gold queries on ARB v2 and is disabled by default on the current development branch.* +4. **Token-Budget Knapsack Optimization**: Uses marginal utility density packing with multi-tier structural skeletons (extracting signatures, docstrings, and query matches) to fit more relevant context into fixed token budgets (2k, 4k, 8k tokens). *The original "3-4x more files" figure was not backed by a recorded measurement and has been removed.* --- -## Verified Benchmark Results +## Benchmark Results (v0.1.0 synthetic benchmark — historical) + +Evaluated on the synthetic **RepoNeedle-Bench** (`benchmarks/runner.py`): **15 samples** (12 positive, 3 selective) across small hand-built corpora, including a held-out repository. These are small toy corpora, not real-world repositories. -Evaluated on **RepoNeedle-Bench** against standard baselines across multiple repositories, including a clean held-out evaluation repository: +**As originally published with v0.1.0** (stored in `benchmarks/results/benchmark_results.json`; *not reproducible by later code*): -| Retrieval System | MRR | Recall@1 | Recall@3 | nDCG@3 | Irrelevant Tokens (4k) | Selective Abstention | Latency | +| Retrieval System | MRR | Recall@1 | Recall@3 | nDCG@3 | Irrelevant Tokens (4k) | Selective (N=3) | Latency | | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | -| **RepoMap (PageRank)** | 0.394 | 0.083 | 0.403 | 0.314 | 926.7 | 0.0% | 0.03 ms | -| **Dense Semantic Vector** | 0.771 | 0.375 | 0.833 | 0.731 | 926.7 | 0.0% | 0.63 ms | -| **Static Hybrid (BM25+Dense+PR)** | 0.743 | 0.375 | 0.875 | 0.746 | 926.7 | 0.0% | 0.65 ms | -| **BM25 (Sparse Lexical)** | 0.819 | 0.417 | 0.861 | 0.784 | 693.6 | 33.3% | 0.15 ms | -| **RepoNeedle (Adaptive + Budget)** | **0.917** | **0.528** | **1.000** | **0.936** | **581.0** | **100.0%** | **0.80 ms** | - -### Key Improvements: -- **+11.9% higher MRR** and **+26.6% higher Recall@1** than the strongest baseline. -- **100% Recall@3** on all positive retrieval tasks. -- **37.3% reduction in irrelevant tokens** (context bloat). -- **100% Selective Abstention Accuracy** on counterfactual queries. -- **Sub-millisecond execution (0.8 ms)** running 100% offline on standard CPU. +| **RepoMap (PageRank)** | 0.394 | 0.083 | 0.403 | 0.314 | 926.7 | 0/3 | 0.03 ms | +| **Dense Semantic Vector** | 0.771 | 0.375 | 0.833 | 0.731 | 926.7 | 0/3 | 0.63 ms | +| **Static Hybrid (BM25+Dense+PR)** | 0.743 | 0.375 | 0.875 | 0.746 | 926.7 | 0/3 | 0.65 ms | +| **BM25 (Sparse Lexical)** | 0.819 | 0.417 | 0.861 | 0.784 | 693.6 | 1/3 | 0.15 ms | +| **RepoNeedle (Adaptive + Budget)** | 0.917 | 0.528 | 1.000 | 0.936 | 581.0 | 3/3 | 0.80 ms | + +**Re-run on 2026-11-09** with the same harness and data. Identical values from the released 1.1.0 code and from the current development branch (experimental abstention explicitly enabled for the 3 selective samples only): + +| Retrieval System | MRR | Recall@1 | Recall@3 | nDCG@3 | Irrelevant Tokens (4k) | Selective (N=3) | +| :--- | :---: | :---: | :---: | :---: | :---: | :---: | +| **RepoMap (PageRank)** | 0.394 | 0.083 | 0.403 | 0.314 | 926.7 | 0/3 | +| **Dense Semantic Vector** | 0.771 | 0.375 | 0.833 | 0.731 | 926.7 | 0/3 | +| **Static Hybrid (BM25+Dense+PR)** | 0.743 | 0.375 | 0.875 | 0.753 | 926.7 | 0/3 | +| **BM25 (Sparse Lexical)** | 0.806 | 0.417 | 0.861 | 0.779 | 693.6 | 1/3 | +| **RepoNeedle (Adaptive + Budget)** | **0.861** | **0.500** | **0.903** | **0.846** | 1042.5 | 2/3 | + +### Observations (corrected) +- On this small synthetic benchmark RepoNeedle has the highest MRR, Recall@1, Recall@3 and nDCG@3 in both the original and the re-run results. +- In the re-run, RepoNeedle does **not** have the fewest irrelevant tokens (BM25 is lower), and Recall@3 is 0.903, not 100%. +- Selective accuracy on a 3-sample synthetic set is not evidence of reliable abstention. On the real ARB v2 selective split (N=82) the heuristic abstained on **0/82**. +- Synthetic-corpus latencies (sub-millisecond) do not transfer to real repositories (ARB v2 mean ~318 ms per query). --- @@ -70,7 +89,7 @@ result = engine.retrieve("Find unit tests for SecureCookieSessionInterface", bud for f in result.selected_files: print(f"Rank {f.path} [{f.tier.value.upper()}] - Tokens: {f.token_count} - Score: {f.score:.3f}") -# Output: +# Illustrative output (not a recorded run): # Rank tests/test_sessions.py [FULL] - Tokens: 165 - Score: 1.000 (Inverted test linkage) # Rank src/flask/sessions.py [FULL] - Tokens: 240 - Score: 0.875 (Target implementation) ``` diff --git a/docs/BENCHMARK_CONFIGURATION.md b/docs/BENCHMARK_CONFIGURATION.md new file mode 100644 index 0000000..ab1ff1d --- /dev/null +++ b/docs/BENCHMARK_CONFIGURATION.md @@ -0,0 +1,165 @@ +# Benchmark Configuration & Reproducibility + +This document records **how each benchmark and research script configures RepoNeedle**, which historical results were measured under which defaults, and known reproducibility gaps. It exists because the development branch (planned 1.2.0, unreleased) changed a default that some evaluations depend on. + +*Last audited: 2026-11-09. Machine-checked by `tests/test_documentation_consistency.py`.* + +--- + +## 1. The Default That Changed + +| Version | Selective abstention heuristic | +| :--- | :--- | +| ≤ 1.1.0 (released) | **Always on.** Every `retrieve()` call could abstain (return no files) on heuristic grounds. | +| Development branch (planned 1.2.0) | **Off by default.** Enabled only via `BudgetConfig(enable_experimental_abstention=True)`, `retrieve(..., enable_experimental_abstention=True)`, the CLI flag `--enable-experimental-abstention`, or the MCP argument of the same name. | + +The heuristic itself (formula, threshold semantics, default threshold `0.12`) is unchanged. Empty repositories always return `abstained=True` regardless of the flag, because there are no candidates. + +**To reproduce 1.1.0 behaviour exactly** in any script, construct the engine with `budget_config=BudgetConfig(enable_experimental_abstention=True)`. + +## 2. Policy + +1. **Scripts that measure the heuristic** (selective/no-gold accuracy, false abstentions on positive queries, or abstainer ablations) **enable it explicitly**, with the original threshold, data and methodology. +2. **Scripts that measure ordinary retrieval** (ranking quality, packing, latency, memory) **use the new default** (disabled), unless they also record per-query abstention decisions. +3. Historical result files are **never regenerated or edited** to match new code. Gaps are documented in Section 6. + +## 3. Scripts That Enable the Experimental Heuristic + + +| Script | What it measures | How abstention is enabled | +| :--- | :--- | :--- | +| `benchmarks/runner.py` | Synthetic RepoNeedle-Bench, including `SelectiveAccuracy` on 3 no-gold samples | Per call, **selective samples only** (`enable_experimental_abstention=is_selective`) | +| `benchmarks/run_ablations.py` | Synthetic ablation study, including the "No Abstainer" factor | Engine-wide (`BudgetConfig`), all samples | +| `benchmarks/run_arb_comprehensive_validation.py` | ARB v0.1 validation; records `abstained`/`confidence` per positive sample plus the N=82 selective pilot | Engine-wide (`BudgetConfig`), all samples | +| `benchmarks/run_complete_arb_evaluation.py` | Headline ARB v2 evaluation (README Parts A–B) | **Phase 2 (selective, N=82) only**; Phase 1 (positive, N=345) uses the default | +| `research/stage3_6/scripts/evaluate_abstention.py` | Stage 3.6 abstention diagnostic (Candidate F configuration) | Engine-wide (`BudgetConfig`) | +| `research/stage3_6/scripts/test_abstention.py` | Abstention decisions on positive and no-gold ARB samples | Engine-wide (`BudgetConfig`) | +| `scripts/verify_abstention_reproducibility.py` | Read-only check that historical ARB v2 abstention and positive results still reproduce | Per call; runs each query **both** with and without the opt-in and compares | + + +## 4. Scripts That Read Abstention Results but Intentionally Use the Default + + +| Script | Reason | +| :--- | :--- | +| `research/stage3_20/run_parity_tests.py` | Only checks the structural empty-repository result (`abstained=True` with zero files), which is independent of the flag | +| `benchmarks/run_arb_current_engine.py` | Ordinary positive-retrieval measurement of the current engine; records `abstained` only to confirm it stays `False` under the default | + + +## 5. Other Scripts (Ordinary Retrieval, Latency, Memory) + +These call `RepoNeedleEngine.retrieve()` / `query()` for ranking, packing or timing and **use the default** (disabled): + +| Category | Scripts | +| :--- | :--- | +| Supplemental diagnostics (experimental, not official) | `benchmarks/run_arb_evaluator_audit.py` (Phase 0 forensic audit), `benchmarks/run_arb_product_aligned_eval.py` (Phase 1 product-aligned evaluation; see Section 9), `benchmarks/run_doc_filter_validation.py` (Phase 2 documentation-filter held-out validation), `benchmarks/run_lexical_forensics.py` (Phase 3 lexical forensics, development data only) | +| Ranking / packing quality | `benchmarks/run_arb_official_eval.py`, `research/contextbench_evaluation/adapter.py`, `research/run_stage2_failure_diagnostics.py`, `research/stage3/benchmark_neural_track.py`, `research/stage3/evaluate_architectural_tradeoffs.py`, `research/stage3/run_contextbench_evaluation.py`, `research/stage3/run_swe_bench_evaluation.py`, `research/stage3_2/evaluate_candidates.py`, `research/stage3_3/run_stage3_3_eval.py`, `research/stage3_5/evaluate_graph_retrieval.py`, `research/stage3_20/run_production_arb_reproduction.py` | +| Latency / memory | `research/measure_runtime_costs.py`, `research/stage3/audit_runtime_and_memory.py`, `research/stage3_5/audit_memory_reduction.py`, `research/stage3_6/audit_runtime_and_memory.py` (and its identical copy under `scripts/`), `research/stage3_9/audit_runtime_and_memory.py`, `research/stage3_12/run_stage3_12_benchmarks.py`, `research/stage3_13/run_stage3_13_audit.py`, `research/stage3_20/benchmark_resources.py`, `research/stage3_21/benchmark_isolated_memory.py`, `scripts/verify_multi_scale_token_and_latency.py`, `scripts/verify_token_savings_and_latency.py` | + +Notes: +- Scripts that read only `ranked_files` (e.g. `run_production_arb_reproduction.py`) are unaffected by abstention in every version: `ranked_files` was populated even when the heuristic abstained. +- In ≤ 1.1.0, an abstained query skipped context packing, so it returned slightly faster. Latency scripts may therefore measure marginally higher times under the new default *if* the heuristic used to fire on their queries. On the ARB v2 positive split it fired on **0 of 345** queries (Section 7.1). +- Scripts that evaluate abstention policies **without** the engine's opt-in path, by computing their own features or thresholds from the engine's indices, are unaffected: `research/run_stage2_abstention_evaluation.py` and `research/stage3_11/run_stage3_11_evaluation.py` (which applies its own `top_score < 3.0` rule). +- All remaining scripts that construct a `RepoNeedleEngine` use its indices or rankers directly and never call `retrieve()`, so the default does not affect them. + +## 6. Historical Results and the Defaults They Were Measured Under + +| Result | Producer | Measured with abstention | Reproducible on the development branch? | +| :--- | :--- | :--- | :--- | +| README Part A (ARB v2 positive, N=345) | `benchmarks/run_complete_arb_evaluation.py` → `reports/full_arb_validation/`, run on 2026-10-28 with the engine at commit `8e00425` (tag `v0.1.0-baseline`) | Always on | **Only with that engine** (verified exactly). Engine changes before the 1.1.0 release changed the rankings, so these numbers do **not** describe 1.1.0 or later. The current engine is re-measured in `reports/arb_v2_current_engine/` (Section 7.3). | +| README Part B (ARB v2 selective, 0/82) | same, Phase 2 | Always on | **Yes**: 0/82 abstained with explicit opt-in on 1.1.0 and on the development branch (confidence values differ slightly from the 2026-10-28 engine, max 0.031, but no decision changes). | +| `benchmarks/results/benchmark_results.json` (synthetic, v0.1.0) | `scripts/run_benchmarks.py` → `benchmarks/runner.py` | Always on | **No.** Stale since v0.1.0: neither 1.1.0 nor the development branch reproduces it (MRR 0.917 stored vs 0.861 re-run; selective 3/3 stored vs 2/3 re-run). 1.1.0 and the development branch produce *identical* values to each other. | +| `benchmarks/results/ablation_study_results.json`, `arb_comprehensive_validation.json` | `run_ablations.py`, `run_arb_comprehensive_validation.py` | Always on | Harnesses opt in explicitly, so the abstention condition is preserved; not re-run in this audit. | +| Stage 3.x research results (`research/stage3_*`) | scripts listed in Section 5 | Always on | Ordinary-retrieval results are unaffected wherever the heuristic did not fire; not individually re-run in this audit. | + +## 7. Verification Performed (2026-11-09) + +All runs used the local ARB v2 checkout (`external/agent-retrieval-bench`), the same data and document construction as `run_complete_arb_evaluation.py`, and were read-only with respect to `reports/full_arb_validation/`. + +### 7.1 The abstention default change does not alter any ARB v2 result + +`python scripts/verify_abstention_reproducibility.py --positives` on the development branch (elapsed 4,299 s): + +| Check | Result | +| :--- | :--- | +| Selective (N=82): abstain decision with explicit opt-in equals the historical decision | **82 / 82** (0/82 abstained, as published) | +| Selective (N=82): abstentions under the new default | **0 / 82** | +| Positive (N=345): heuristic would abstain when opted in | **0 / 345** | +| Positive (N=345): selected files identical with and without opt-in | **345 / 345** | + +Conclusion: making the heuristic opt-in changes no positive-query result and no selective decision on ARB v2. + +### 7.2 Historical results describe the 2026-10-28 engine, not 1.1.0 + +The same script reported that current confidence values (2/82) and top-20 lists (5/345) mostly do **not** equal the historical records. To attribute this, the same inputs were run against three code versions (all 82 selective queries plus the 53 positive queries of the first 25 snapshots): + +| Engine | Selective confidence equal to history | Positive top-20 equal to history | +| :--- | :---: | :---: | +| Commit `8e00425` (tag `v0.1.0-baseline`, 2026-10-28; produced the historical reports) | **82 / 82** (bit-identical) | **53 / 53** | +| Released 1.1.0 (`HEAD` of `main`) | 2 / 82 (max diff 0.031) | 1 / 53 | +| Development branch (planned 1.2.0) | identical to 1.1.0 (max diff 0.0) | identical to 1.1.0 (53 / 53) | + +Conclusions: +- The evaluation pipeline and data are sound: the producing engine reproduces the published records exactly on every query checked. +- Engine changes made between 2026-10-28 and the 1.1.0 release changed RepoNeedle's rankings. **README Part A and the ARB-derived claims in `CLAIMS_AND_EVIDENCE.md` describe the 2026-10-28 engine.** +- Milestones 1A–1C (CLI JSON, anchors, MCP, abstention opt-in, indexing diagnostics) changed no ranking result: the development branch is bit-identical to 1.1.0 on every query compared. + +### 7.3 Current-engine re-measurement (ARB v2 positive, N=345) + +`benchmarks/run_arb_current_engine.py` re-measures RepoNeedle only, replicating the harness's RepoNeedle path. It was validated by running it against the `v0.1.0-baseline` engine on the first 12 snapshots: all 8 metrics and the top-20 list reproduced exactly for 20 / 20 samples. Official BM25 / Lexical / RepoMap baselines do not depend on RepoNeedle code, so their historical per-sample values are used for comparison. Results: `reports/arb_v2_current_engine/SUMMARY.json`. + +Run on 2026-11-09 in 3 parallel shards (engine version 1.1.0 = development branch; 345 / 345 samples; 0 abstentions). + +| System (N=345) | MRR | Recall@5 | Recall@10 | Recall@20 | BCY@4k | BCY@8k | BCY@16k | BCY@32k | +| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| **RepoNeedle, current engine (1.1.0 / dev branch)** | **0.2153** | **0.3032** | **0.3677** | **0.3842** | **0.2400** | **0.3443** | **0.3813** | **0.3842** | +| RepoNeedle, 2026-10-28 engine (README Part A) | 0.2287 | 0.3011 | 0.4110 | 0.4279 | 0.2436 | 0.3450 | 0.4192 | 0.4279 | +| Official BM25 | 0.1520 | 0.1899 | 0.3034 | 0.4452 | 0.1220 | 0.2051 | 0.3184 | 0.4365 | +| Official Lexical | 0.1574 | 0.2292 | 0.3548 | 0.4940 | 0.1488 | 0.2650 | 0.3886 | 0.4882 | +| Official RepoMap | 0.2140 | 0.3185 | 0.4786 | 0.6306 | 0.2024 | 0.3788 | 0.5235 | 0.6166 | + +Paired MRR differences (current engine minus comparator; 5,000 bootstrap resamples; repository-clustered across 25 repositories): + +| Comparison | Mean Δ MRR | Wins / Ties / Losses | Sample 95% CI | Repo-clustered 95% CI | +| :--- | :---: | :---: | :---: | :---: | +| vs BM25 | +0.0633 (+41.6%) | 132 / 17 / 196 | [+0.0312, +0.0948] | [+0.0111, +0.0980] | +| vs Lexical | +0.0579 (+36.8%) | 118 / 24 / 203 | [+0.0278, +0.0888] | [+0.0180, +0.0933] | +| vs RepoMap | +0.0013 (+0.6%) | 106 / 27 / 212 | [−0.0330, +0.0359] | [−0.0597, +0.0517] | +| vs 2026-10-28 RepoNeedle engine | −0.0134 | 85 / 175 / 85 | [−0.0205, −0.0065] | [−0.0204, −0.0061] | + +MRR by task (current engine vs official baselines): + +| Task | N | Current RepoNeedle | 2026-10-28 RepoNeedle | BM25 | Lexical | RepoMap | +| :--- | :---: | :---: | :---: | :---: | :---: | :---: | +| `code2test` | 106 | 0.2546 | 0.2660 | 0.1057 | 0.0663 | 0.1942 | +| `comment2context` | 80 | 0.1658 | 0.1708 | 0.1974 | 0.1530 | 0.1532 | +| `trace2code` | 101 | 0.2262 | 0.2337 | 0.1638 | 0.2075 | 0.2723 | +| `edit2ripple` | 58 | 0.1929 | 0.2320 | 0.1536 | 0.2428 | 0.2323 | + +Findings: +- The qualitative conclusions published in README Part A still hold for the current engine: significantly better MRR than BM25 and Lexical (repository-clustered CIs exclude zero), statistical parity with RepoMap on MRR, a large RepoMap advantage in deep recall (Recall@20: 0.6306 vs 0.3842), the strongest `code2test` MRR (+140.8% vs BM25, +31.0% vs RepoMap), and a `comment2context` deficit against BM25 (−16.0%, previously −13.5%). The BCY@4k advantage over RepoMap is +18.5% (previously +20.4%); no confidence interval was computed for BCY. +- The current engine is measurably weaker than the 2026-10-28 engine on MRR (−0.0134, CI excludes zero), mainly through lower Recall@10 / @20. Note that the harness ranks every file RepoNeedle did not pack in corpus order (it uses `selected_files`, not `ranked_files`), so deep-rank metrics also depend on how many files the packer selects at an 8,000-token budget. This is a hypothesis for follow-up, not a verified cause. +- Latency (mean 269.8 ms, p50 188.3 ms, p95 775.5 ms) was measured with three shards running concurrently and is **not comparable** to the single-process latency figures in README Part D. + +## 8. Research Integrity Notes + +Issues found during the audit. The affected files are **left unchanged** for the record; errata were added where the results are reported. + +1. **Stage 3.6 synthesized RepoMap values.** `research/stage3_6/scripts/evaluate_systems.py` (and its identical copy one directory up) writes `PER_QUERY_RESULTS.csv`, in which `BASE-REPOMAP` `bcy_at_4k` = 0.5 × `bcy_at_8k` and `bcy_at_16k` = min(1, 1.5 × `bcy_at_8k`) (all 29 rows with non-zero BCY@8k) and `latency_ms` is the constant 180.20 (all 345 rows). The `abstained` column is hard-coded `False` for every system. A fallback branch would also fill missing RepoMap records with published aggregate values (it was not triggered: 0 rows match). RepoMap MRR/Recall come from official ARB records. **The README's headline numbers are not affected**: they come from `run_complete_arb_evaluation.py`, which measures BCY for every system with the official `pack_files`. +2. **Stage 3.6 simulated positive confidences.** `research/stage3_6/scripts/evaluate_abstention.py` does not run the 345 positive queries. It hard-codes `abstained = 0` and draws confidence values from a normal distribution fitted to ~35 sampled queries. The positive-row values in `ABSTENTION_RESULTS.csv` (mean confidence, Brier score) and the overall Brier/ECE are therefore simulated. The 82 no-gold rows were measured. An erratum was added to `research/stage3_6/REPRODUCIBILITY_REPORT.md`. +3. **Synthetic "100% selective abstention" claim.** The v0.1.0 announcement's 100% figure came from 3 synthetic samples in the stale `benchmark_results.json`. It has been corrected in `docs/ANNOUNCEMENT.md` and recorded as retracted claim C11 in `CLAIMS_AND_EVIDENCE.md`. +4. **Documented threshold and score ranges.** Earlier docs stated a default threshold of 0.38 and a confidence range of [0.64, 0.86]. The engine default has always been 0.12 (0.38 is the unused class default), and the measured range on the 82 no-gold queries is [0.532, 0.891]. Corrected in README, KNOWN_LIMITATIONS and CLAIMS_AND_EVIDENCE. +5. **Hard-coded absolute paths.** Six research scripts still hard-code `F:\RepoNeedle` and will not run unchanged on another machine: `research/stage3_6/scripts/generate_graph_csv.py`, `research/stage3_10/generate_canonical_results.py`, `research/stage3_11/run_stage3_11_audit.py`, `research/stage3_12/run_stage3_12_benchmarks.py`, `research/stage3_19/run_clean_reproduction.py`, `research/stage3_20/run_candidate_f_forensics.py`. The two abstention scripts edited in this audit now resolve the repository root relative to their own location. + +## 9. Kinds of ARB v2 Results (Do Not Mix Them) + +| Kind | What it is | Engine | Where | Status | +| :--- | :--- | :--- | :--- | :--- | +| **Official ARB v2 result (historical, published)** | Official harness: RepoNeedle gets one fragment per file; packed files then corpus order are scored | Commit `8e00425` (tag `v0.1.0-baseline`, 2026-10-28) | README Part A; `reports/full_arb_validation/` | Official. Reproducible with that engine. Does not describe 1.1.0 | +| **Official harness, current engine** | The same harness and metric definitions, re-run | 1.1.0 = development branch | `reports/arb_v2_current_engine/`; this file §7.3 | Official-harness measurement of today's engine | +| **Phase 0 forensic audit** | Diagnostic decomposition (full ranking vs packing, document representation, documentation distractors) | 1.1.0 = development branch | `reports/experimental/phase0_evaluator_audit/` | Experimental diagnostic | +| **Phase 2 documentation-filter validation** | Pre-registered repository-level dev/test experiment on the existing opt-in `enable_doc_filter` (penalty 0.5) using the supplemental framework | 1.1.0 = development branch | `reports/experimental/phase2_documentation_filter/` | Experimental. Result: **NOT VALIDATED** on its primary endpoint (comment2context Recall@20 Δ = 0.000); no guardrail regression; filter remains off by default | +| **Phase 3 lexical retrieval forensics** | Pre-registered development-only ablations of RepoNeedle's lexical index (query term frequency, duplicate tokens, JSON keys, length normalisation, field weights) | 1.1.0 = development branch | `reports/experimental/phase3_lexical_retrieval/` | Experimental, development evidence only. Root cause identified (query term frequency ignored); no change met the dev-support criterion; **NOT VALIDATED**; no fresh confirmatory data available | +| **Supplemental product-aligned evaluation** | File-unit full-ranking metrics on fragment **and** whole-file input for RepoNeedle and BM25, matched comparisons, packed-context coverage reported separately, no remainder positions | 1.1.0 = development branch | `reports/experimental/phase1_product_aligned_evaluation/` (`benchmarks/run_arb_product_aligned_eval.py`) | **Supplemental. Not an official ARB score** | + +Supplemental headline (345 queries, 25 repositories; paired, repository-clustered 95% CIs). On matched whole-file input, RepoNeedle's full ranking exceeds BM25 overall: MRR 0.2789 vs 0.1717 (+0.1073 [+0.0647, +0.1596]); Recall@20 0.7042 vs 0.5138 (+0.1904 [+0.1114, +0.2715]). By task, code2test is significantly better on every metric; edit2ripple is significantly better on Recall@10/20; trace2code is inconclusive (7 repositories); **comment2context is level on MRR but significantly worse on Recall@20 (−0.0896 [−0.1779, −0.0056])**. Full details, fidelity checks and limitations are in that directory's `REPORT.md`. diff --git a/docs/MCP_INTEGRATION.md b/docs/MCP_INTEGRATION.md new file mode 100644 index 0000000..ee949f6 --- /dev/null +++ b/docs/MCP_INTEGRATION.md @@ -0,0 +1,335 @@ +# RepoNeedle Model Context Protocol (MCP) Integration Guide + +**Status**: Available on the development branch; **planned for RepoNeedle 1.2.0 (not yet released)**. The published PyPI release (1.1.0) does **not** include the MCP server. +**Transport**: STDIO JSON-RPC 2.0 (HTTP/SSE transport is not implemented) +**SDK Requirement**: `mcp>=1.28,<2` (Optional dependency group: `mcp`) +**Validation**: Verified with the MCP Python SDK (in-process tool calls) and a raw JSON-RPC STDIO handshake (`initialize`, `tools/list`, `tools/call`) in the test suite. **Not yet validated in a live Claude Code, Cursor, Cline or Windsurf session**; the client configuration examples below follow each client's documented MCP configuration format. + +--- + +## 1. Overview: What is MCP and Why RepoNeedle Supports It + +The **Model Context Protocol (MCP)** is an open specification enabling AI coding agents (such as Claude Code, Cursor, Cline, and Windsurf) to securely access local tools, file systems, and specialized retrieval engines through a standardized JSON-RPC 2.0 protocol. + +### Why RepoNeedle Fits MCP +1. **CPU-Only Execution**: Runs locally on commodity CPUs without GPUs, API keys or network calls. On the ARB v2 benchmark, per-query latency after indexing was ~142 ms median and ~318 ms mean (repository indexing is a separate one-time cost at server startup). +2. **Token Budget Invariant**: Returned context satisfies + $$C_{\text{actual}} \le B_{\text{configured}}$$ + as counted by RepoNeedle's internal regex token model. Counts from a specific LLM tokenizer may differ, so leave headroom in tight context windows. +3. **Change-Impact Analysis**: Ranks downstream files, modules and regression tests related to files an agent has edited. +4. **Optional Dependency**: MCP support is packaged as an **optional extra** (`mcp`). Users who do not need MCP do not install the MCP SDK; the core engine keeps its standard dependencies (numpy, scipy, networkx, pydantic, pathspec, rich). + +--- + +## 2. Installation + +RepoNeedle core requires Python 3.10+. MCP support is currently available **from source only** (development branch, planned for 1.2.0): + +```bash +git clone https://github.com/krishika08/RepoNeedle.git +cd RepoNeedle +pip install -e ".[mcp]" +``` + +> [!NOTE] +> `pip install "reponeedle[mcp]"` from PyPI currently installs **1.1.0**, which does not contain the MCP server or the `mcp` extra (pip only warns about the unknown extra). Use the PyPI command once a release that includes MCP support has been published. + +To verify that the MCP server executable is available: + +```bash +reponeedle-mcp --help +``` + +--- + +## 3. Server Launch Instructions + +The RepoNeedle MCP server operates locally over the standard **STDIO** transport. + +### Running via Console Script +```bash +reponeedle-mcp --repo /absolute/path/to/repository --budget 4000 +``` + +### Running via Python Module +```bash +python -m reponeedle.mcp --repo /absolute/path/to/repository --budget 4000 +``` + +### CLI Options + +| Option | Flag | Default | Description | +| :--- | :---: | :---: | :--- | +| `--repo` | `-r` | `.` | Absolute or relative path to the target repository root. | +| `--budget` | `-b` | `4000` | Default token budget for retrieved context blocks. | +| `--transport` | | `stdio` | Transport protocol (`stdio`). | +| `--no-skeletons` | | `False` | Require full file bodies; disallow structural skeleton mode. | +| `--max-file-size-bytes` | | `500000` | Skip files strictly larger than this many bytes during the startup index. Must be a positive integer (zero/negative exits with code `1`). Skipped paths are reported by `reponeedle_inspect`. | + +If files are excluded at startup, the server writes a one-line summary to `STDERR`, e.g. `[reponeedle-mcp] Indexing exclusions: 2 file(s) over 500000 bytes, 0 unreadable. Call reponeedle_inspect for the skipped paths.` + +> [!IMPORTANT] +> **STDIO Cleanliness**: The MCP server sends all initialization logs and diagnostics exclusively to `STDERR`. `STDOUT` carries strictly formatted JSON-RPC messages, ensuring zero protocol framing corruption. + +--- + +## 4. Client Configuration Examples + +### 4.1 Claude Code CLI Integration + +To add RepoNeedle as an MCP tool server in **Claude Code**, run (the `--` separates Claude Code's own options from the server command and its arguments): + +```bash +claude mcp add reponeedle -- python -m reponeedle.mcp --repo /absolute/path/to/your/project +``` + +Or add an `mcpServers` entry to a Claude Code MCP configuration file (see the Claude Code documentation for the project- and user-scope file locations): + +```json +{ + "mcpServers": { + "reponeedle": { + "command": "python", + "args": [ + "-m", + "reponeedle.mcp", + "--repo", + "/absolute/path/to/your/project", + "--budget", + "4000" + ] + } + } +} +``` + +### 4.2 Cursor IDE Integration + +In Cursor: +1. Open **Cursor Settings** $\to$ **Features** $\to$ **MCP**. +2. Click **+ Add New MCP Server**. +3. Configure the server: + - **Name**: `reponeedle` + - **Type**: `command` + - **Command**: `python -m reponeedle.mcp --repo /absolute/path/to/your/project` + +Or edit `.cursor/mcp.json`: + +```json +{ + "mcpServers": { + "reponeedle": { + "command": "python", + "args": [ + "-m", + "reponeedle.mcp", + "--repo", + "/absolute/path/to/your/project" + ] + } + } +} +``` + +--- + +## 5. Available MCP Tools + +RepoNeedle exposes three tools to connected agents: + +### 5.1 `reponeedle_retrieve` +Retrieves relevance-ranked source files, focus spans, and structural skeletons matching a natural language coding task, strictly packed within the requested token budget. + +#### Input Schema +```json +{ + "query": "Fix authentication session expiration timeout", + "budget_tokens": 4000, + "ranker": "legacy", + "task_type": "auto", + "anchor_files": ["auth/session.py"], + "enable_experimental_abstention": false +} +``` + +- `query` (*string*, required): Coding task, issue description, or traceback. +- `budget_tokens` (*integer*, default `4000`): Maximum context tokens. +- `ranker` (*string*, default `"legacy"`): Ranking algorithm (`legacy`, `bm25`, `arm101`, `cascade`, `change_impact`). +- `task_type` (*string*, default `"auto"`): Workflow intent (`auto`, `code2test`, `comment2context`, `trace2code`, `edit2ripple`, `selective`). `selective` does **not** enable abstention. +- `anchor_files` (*array of strings*, optional): Seed files used to condition retrieval. +- `enable_experimental_abstention` (*boolean*, default `false`): Opt in to the **experimental, uncalibrated** out-of-scope abstention heuristic. When `false`, the tool always returns ranked results (except for an empty repository) and does not detect out-of-scope queries. See [Section 5.4](#54-experimental-abstention). + +#### Return Payload +```json +{ + "status": "success", + "query": "Fix authentication session expiration timeout", + "ranker": "legacy", + "task_type": "comment2context", + "abstained": false, + "explanation": "Retrieved 3 files (1420/4000 tokens). Ranker: legacy. Task: comment2context. Experimental abstention disabled.", + "budget_tokens": 4000, + "total_tokens": 1420, + "execution_time_ms": 12.4, + "confidence_score": 0.84, + "selected_files": [ + { + "rank": 1, + "path": "auth/session.py", + "tier": "focus_span", + "token_count": 450, + "score": 0.8124, + "explanation": "High-density implementation span" + } + ], + "rendered_context": "# File: auth/session.py\n```python\n...\n```\n\n", + "abstention": { + "enabled": false, + "status": "disabled", + "experimental": true, + "calibrated": false, + "threshold": 0.12, + "reason": "Experimental abstention disabled (default); out-of-scope queries are not detected and results are returned regardless of query scope." + } +} +``` + +`confidence_score` is an uncalibrated heuristic signal, not a probability that the repository contains the answer. + +--- + +### 5.2 `reponeedle_change_impact` +Identifies downstream files, dependent modules, and regression test suites affected by modifications to specific files. Implements verified R6 champion algorithms: +- **Seed File Suppression (SFS)**: Downweights seed files ($0.01\times$) so the agent is not re-fed files it already modified. +- **Test Intent Priors (TIP)**: Dynamically boosts associated regression test suites ($1.45\times$). +- **AST Dependency Traversal**: Confirmed imports, function calls, and directory proximity. + +#### Input Schema +```json +{ + "anchor_files": [ + "auth/session.py", + "auth/token.py" + ], + "query": "Assess authentication refactor impact", + "budget_tokens": 4000 +} +``` + +- `anchor_files` (*array of strings*, required): One or more modified or seed file paths. +- `query` (*string*, optional): Context description or task intent. +- `budget_tokens` (*integer*, default `4000`): Maximum context tokens. + +--- + +### 5.3 `reponeedle_inspect` +Enables the coding agent to inspect the indexed repository state without dumping full source trees. + +#### Input Schema +Takes no arguments (`{}`). + +#### Return Payload +```json +{ + "status": "ready", + "repository_root": "/absolute/path/to/project", + "indexed_file_count": 148, + "supported_file_types": [ + ".py", ".ts", ".js", ".tsx", ".jsx", + ".go", ".rs", ".java", ".c", ".cpp", + ".h", ".md", ".toml", ".yaml", ".yml", ".json" + ], + "indexed_path_examples": [ + "auth/session.py", + "auth/token.py", + "database/connection.py" + ], + "sample_count": 3, + "active_rankers": ["arm101", "bm25", "cascade", "change_impact", "legacy"], + "default_ranker": "legacy", + "budget_config": { + "max_tokens": 4000, + "allow_skeletons": true, + "mode": "auto" + }, + "index_initialization": { + "status": "initialized", + "init_time_ms": 145.2, + "message": "Repository indexed in-memory at server startup. Restart server to refresh index." + }, + "indexing": { + "source": "directory", + "indexed_files": 148, + "max_file_size_bytes": 500000, + "skipped_large_files": 2, + "skipped_paths": ["generated/large_module.py", "vendor/huge_source.cpp"], + "skipped_unreadable_files": 0, + "unreadable_paths": [], + "skipped_symlink_outside_repository": 0, + "symlink_outside_repository_paths": [], + "max_files": 3000, + "max_files_reached": false + }, + "abstention": { + "default_enabled": false, + "experimental": true, + "calibrated": false, + "threshold": 0.12, + "opt_in": "Pass enable_experimental_abstention=true to reponeedle_retrieve." + } +} +``` + +#### Indexing Diagnostics +The `indexing` object reports what the startup index excluded, so an agent can tell when a file it expects is missing: +- `skipped_paths` / `skipped_large_files`: supported-extension files strictly larger than `max_file_size_bytes` (set with `--max-file-size-bytes`). A file exactly at the limit is indexed. +- `unreadable_paths` / `skipped_unreadable_files`: files that could not be read (including broken links and link loops). +- `symlink_outside_repository_paths` / `skipped_symlink_outside_repository`: in-repository links whose target resolves outside the repository root. Their targets are never read; only the link path is listed. +- `max_files_reached`: `true` if indexing stopped at the `max_files` cap. + +Paths are repository-relative with forward slashes and sorted. File contents are never included. Files excluded by `.gitignore`, built-in ignore patterns, or unsupported extensions are excluded by design and are not listed. Diagnostics describe the index built at server startup; restart the server to refresh them. + +--- + +### 5.4 Experimental Abstention +RepoNeedle includes a heuristic that can abstain (return no files) when a query appears to be out of scope for the repository. It is **experimental and disabled by default**: + +- **Why it is experimental**: on the official Agent Retrieval Bench v2 selective split it correctly abstained on **0 of 82** negative samples (100% false returns). Natural issues and wrong-repository queries share enough vocabulary with real code that the overlap, peakiness and margin signals do not separate them. +- **Why it is disabled by default**: leaving an uncalibrated filter on suggests that out-of-scope queries are being caught when they are not. With it disabled, agents should treat every result as candidate context, not as proof that the repository contains the answer. +- **How to enable it**: pass `"enable_experimental_abstention": true` to `reponeedle_retrieve`. The returned `abstention.status` is then `"evaluated"`, and an abstained response has `status: "abstained"`, empty `selected_files` and empty `rendered_context`. +- **`task_type: "selective"` does not enable it.** The task label only changes routing weights. +- `reponeedle_change_impact` never applies the heuristic. +- An empty repository always yields `abstained: true` with `abstention.status: "empty_repository"`, independent of the flag. + +--- + +## 6. Security Boundaries + +The RepoNeedle MCP server executes locally with read-only access to the configured repository. It enforces strict boundary controls: + +1. **Explicit Root Configuration**: The repository root is fixed at server launch (`--repo`). Tool calls cannot specify or switch to arbitrary directories. +2. **Path Traversal Protection**: Tool path arguments (e.g. `anchor_files`) whose path climbs above the repository root (a leading `..` path component) or that point outside it are rejected with descriptive validation errors. Names that merely start with `..` (e.g. `..cache/x.py`) are legitimate and accepted. +3. **Symlink Escape Prevention**: Tool path arguments are resolved to their canonical location with `os.path.realpath()` and rejected if that lies outside the repository root. **Indexing applies the same rule**: every file is resolved before it is opened, files whose canonical location is outside the repository (symlinks; junctions on Windows) are skipped without reading the target, and only the in-repository link path is recorded (`symlink_outside_repository_paths`, reason `symlink_outside_repository`). Links that resolve inside the repository are indexed normally. +4. **Zero File Modification**: The server operates strictly read-only. It contains no file write, edit, or delete capabilities. +5. **Credential Safety**: Diagnostics, error messages, and inspection metadata never expose environment variables, API tokens, or secrets. + +--- + +## 7. Operational Lifecycle & Index Refreshing + +1. **Startup Ingestion**: The server parses repository ASTs and builds inverted postings indices **once** upon startup. +2. **In-Memory Query Re-use**: Subsequent tool calls execute against the pre-computed in-memory index, avoiding redundant disk scans. Query latency depends on repository size (small test repositories: a few milliseconds; ARB v2 real repositories: ~142 ms median). +4. **No Persistent Cache or File Watching**: The index is not persisted to disk and the server does not watch the filesystem; both are not implemented. +3. **Refreshing the Index**: When source files on disk change substantially, restart the MCP server process (or restart Claude Code / reload Cursor window) to rebuild the index. + +--- + +## 8. Uninstalling Optional MCP Dependency + +If MCP support is no longer needed: + +```bash +pip uninstall mcp +``` + +RepoNeedle's core CLI (`reponeedle`) and Python API will continue functioning normally with zero missing dependency warnings. diff --git a/docs/ROADMAP.md b/docs/ROADMAP.md index 8713ea0..d6c2f9b 100644 --- a/docs/ROADMAP.md +++ b/docs/ROADMAP.md @@ -1,36 +1,55 @@ # RepoNeedle Research & Engineering Roadmap -This roadmap is grounded in empirical limitations observed during benchmark evaluations and practical deployments. +This roadmap is grounded in empirical limitations observed during benchmark evaluations. Status labels distinguish what has been **released**, what exists only on the **development branch**, and what is **not implemented**. + +*Last reconciled with the source code: 2026-11-09.* --- -## Current Status (v0.1.0) - Completed & Validated -- [x] Multi-signal hybrid retrieval (Field-weighted BM25 + Subword Semantic Vectors + AST Repository Graph). +## Released: RepoNeedle 1.1.0 (PyPI, 2026-11-03) +- [x] Multi-signal hybrid retrieval (field-weighted BM25 + subword semantic vectors + AST repository graph). - [x] Workflow-adaptive routing (`trace2code`, `code2test`, `edit2ripple`, `comment2context`, `selective`). - [x] Inverted test-to-target dependency mapping for `code2test` regression test discovery. -- [x] Calibrated selective abstention engine for no-gold and out-of-repo queries. -- [x] Token-budget knapsack packing with multi-tier structural skeletons. -- [x] Zero-network, offline CPU execution under 1 ms latency. -- [x] Reproducible benchmark suite with clean repository-level held-out split (`RepoNeedle-Bench`). -- [x] 100% test coverage across core modules. -- [x] Hugging Face release assets (Dataset Card, Gradio Playground Space). +- [x] CUD-Pack token-budget packing with multi-tier representations (full files, focus spans, structural skeletons). +- [x] Opt-in `change_impact` ranker (seed-relative relationship scoring). +- [x] CPU-only execution with no GPU or neural-runtime dependencies. Measured mean query latency on real repositories (ARB v2) is ~318 ms; sub-millisecond figures apply only to tiny synthetic corpora. +- [x] Python 3.10, 3.11 and 3.12 (CI matrix). +- [x] Selective abstention heuristic, **always on in 1.1.0**. It is **experimental and uncalibrated**: on the ARB v2 selective split it correctly abstained on **0 of 82** no-gold queries. +- [x] Hugging Face release assets prepared in `hf_artifacts/` (dataset card, Gradio Playground Space bundling the 1.1.0 engine, where abstention is always on). + +## Development branch (planned for 1.2.0, not yet released) +- [x] CLI machine-readable output (`--json`) and anchor files (`--anchors`). +- [x] Native MCP server over STDIO (`reponeedle-mcp`, optional `mcp` extra) with `reponeedle_retrieve`, `reponeedle_change_impact` and `reponeedle_inspect`. Verified with the MCP Python SDK and a raw JSON-RPC STDIO handshake; **not yet validated in a live Claude Code or Cursor session**. +- [x] Experimental abstention made **opt-in (disabled by default)**, with explicit diagnostics (`experimental: true`, `calibrated: false`) on every result. +- [x] Indexing diagnostics: configurable file-size limit (`max_file_size_bytes`, default 500,000 bytes) with recorded skipped and unreadable paths. +- [x] Benchmark reproducibility audit: explicit abstention policy per script ([BENCHMARK_CONFIGURATION.md](BENCHMARK_CONFIGURATION.md)). + +Test coverage is measured, not assumed: on the development branch, line coverage of the `reponeedle` package is **87%** (`pytest --cov=reponeedle`, 2026-11-09). Earlier claims of "100% test coverage" were incorrect. + +## Not implemented +| Capability | Status | +| :--- | :--- | +| Persistent / on-disk index cache | Not implemented (the index is rebuilt in memory on every start) | +| Automatic live filesystem updates / file watching | Not implemented (restart the MCP server to refresh) | +| HTTP / SSE MCP transport | Not implemented (STDIO only) | +| AST block slicing (sub-function spans of large files) | Not implemented | +| Calibrated selective retrieval / reliable abstention | Not implemented (open research problem, below) | --- -## Phase 2: Multi-Language & Protocol Integration (Q1 2027) -- [ ] **Tree-sitter & SCIP Indexing**: - - Add optional Tree-sitter parsers for TypeScript, Go, Rust, Java, and C++ to extend deep AST symbol graphs beyond Python. - - Implement SCIP (Source Code Intelligence Protocol) index ingest for enterprise codebases. -- [ ] **Dynamic Dependency Tracking**: - - Ingest optional pytest / coverage runtime trace logs (`.coverage` or test execution logs) to bridge dynamic dispatch and reflection. -- [ ] **Third-Party Package Boundary Disambiguation**: - - Refine selective abstention to detect when an issue belongs in an external upstream library (e.g., `urllib3` vs `requests`) using `pip freeze` / `pyproject.toml` dependency parsing. - ---- - -## Phase 3: Agent Scaffolding & Framework Adapters (Q2 2027) -- [ ] **Aider Adapter**: Drop-in replacement for Aider's RepoMap to enable workflow routing and test discovery. -- [ ] **SWE-bench / OpenHands / Cline Integration**: MCP (Model Context Protocol) server wrapping RepoNeedle for one-click installation into coding assistants. -- [ ] **Disk-Backed Inverted Indexing**: - - Add optional embedded DuckDB / SQLite backing for giant monorepos (> 100,000 files). - +## Research needed: Reliable Selective Retrieval +The current heuristic (repository vocabulary overlap + $Z$-score peakiness + top-vs-median score margin over the fused ranking scores) does not separate in-scope from out-of-scope queries on real repositories. Its confidence on all 82 ARB v2 no-gold queries ranged from 0.532 to 0.891, far above the engine's default threshold of 0.12. +- [ ] Train and evaluate a dedicated discriminator (e.g. a cross-encoder) on held-out repositories, reporting abstention precision/recall **and** the false-abstention rate on positive queries. +- [ ] Calibrate scores (e.g. temperature or isotonic calibration) and report calibration error before exposing any score as a probability. +- [ ] Keep the heuristic opt-in until a method passes a pre-registered evaluation on ARB v2. +- [ ] Third-party package boundary disambiguation (e.g. `urllib3` vs `requests`) using dependency manifests. + +## Phase 2: Multi-Language & Protocol Integration +- [ ] **Tree-sitter & SCIP Indexing**: optional Tree-sitter parsers for TypeScript, Go, Rust, Java and C++; SCIP index ingest for large codebases. +- [ ] **Dynamic Dependency Tracking**: optional pytest / coverage runtime traces to bridge dynamic dispatch and reflection. +- [ ] **Live client validation** of the MCP server in Claude Code, Cursor and Cline, recorded as reproducible integration tests. + +## Phase 3: Agent Scaffolding & Scale +- [ ] **Aider Adapter**: drop-in alternative to Aider's RepoMap for workflow routing and test discovery. +- [ ] **Persistent, incremental indexing** (on-disk cache; embedded SQLite/DuckDB backing for monorepos > 100,000 files). +- [ ] **AST block slicing** for monolithic files. diff --git a/docs/v1.1_change_impact_release.md b/docs/v1.1_change_impact_release.md index bb8a2b4..2913b3e 100644 --- a/docs/v1.1_change_impact_release.md +++ b/docs/v1.1_change_impact_release.md @@ -1,7 +1,7 @@ # RepoNeedle V1.1.0 Release Notes: Change-Impact Retrieval Engine **Release Version**: `1.1.0` -**Release Status**: V1.1.0 source integrated on main; formal release publication pending +**Release Status**: Published to PyPI on 2026-11-03 (originally written while publication was pending) **Release Date**: November 2026 **Target Branch**: `main` (Merged from `v1.1-production-release` via PR #2 & PR #3) **Compatibility**: 100% backwards-compatible with V1.0.0 diff --git a/pyproject.toml b/pyproject.toml index 1caa19d..e0ae310 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,14 +36,26 @@ dependencies = [ dev = [ "pytest>=7.0.0", "pytest-cov>=4.0.0", + "pytest-asyncio>=0.23", +] +# Retained from the published 1.1.0 release for installation compatibility. Optional only; +# no RepoNeedle module imports these packages. +api = [ + "fastapi>=0.115,<1.0", + "httpx>=0.24", + "uvicorn[standard]>=0.30,<1.0", ] hf = [ "huggingface_hub>=0.20.0", "gradio>=4.0.0", ] +mcp = [ + "mcp>=1.28,<2", +] [project.scripts] reponeedle = "reponeedle.cli:main" +reponeedle-mcp = "reponeedle.mcp.server:main" [tool.setuptools.packages.find] where = ["."] diff --git a/reponeedle/__init__.py b/reponeedle/__init__.py index 293b600..dd22f29 100644 --- a/reponeedle/__init__.py +++ b/reponeedle/__init__.py @@ -11,8 +11,10 @@ RankerType, BudgetConfig, ContextTier, + AbstentionDiagnostics, + IndexingDiagnostics, ) -from reponeedle.engine import RepoNeedleEngine +from reponeedle.engine import RepoNeedleEngine, DEFAULT_MAX_FILE_SIZE_BYTES from reponeedle.core.change_impact import ChangeImpactRanker, ChangeImpactConfig from reponeedle.core.packer import ( CUDPackOptimizer, @@ -32,6 +34,9 @@ "RankerType", "BudgetConfig", "ContextTier", + "AbstentionDiagnostics", + "IndexingDiagnostics", + "DEFAULT_MAX_FILE_SIZE_BYTES", "ChangeImpactRanker", "ChangeImpactConfig", "CUDPackOptimizer", diff --git a/reponeedle/cli.py b/reponeedle/cli.py index 4812919..5c14c8a 100644 --- a/reponeedle/cli.py +++ b/reponeedle/cli.py @@ -3,21 +3,91 @@ """ import sys +import os +import json import argparse +from typing import List, Optional from rich.console import Console from rich.table import Table from rich.panel import Panel -from reponeedle.engine import RepoNeedleEngine +from reponeedle.engine import ( + RepoNeedleEngine, + DEFAULT_MAX_FILE_SIZE_BYTES, + validate_max_file_size_bytes, + is_path_within_root, + relpath_escapes_root, +) from reponeedle.models import TaskType, BudgetConfig +# Maximum number of skipped paths listed in human-readable (non-JSON) stderr notices. +# JSON output always contains the complete list. +_MAX_SKIPPED_PATHS_SHOWN = 20 -def main(): - if hasattr(sys.stdout, "reconfigure"): + +def resolve_and_validate_anchors(repo_dir: str, anchors: Optional[List[str]]) -> List[str]: + """ + Validates and resolves anchor file paths against the repository directory. + Returns normalized repository-relative paths with forward slashes. + Raises ValueError if an anchor does not exist or falls outside the repository. + """ + if not anchors: + return [] + + abs_repo = os.path.normpath(os.path.abspath(repo_dir)) + normalized: List[str] = [] + + for a in anchors: + if not a or not a.strip(): + continue + raw_anchor = a.strip() + + # Check path interpretation 1: Relative to repo_dir (e.g. "auth/session.py" with --repo "dir") + candidate_1 = os.path.normpath(os.path.join(abs_repo, raw_anchor)) + # Check path interpretation 2: Relative to current working directory or absolute + candidate_2 = os.path.normpath(os.path.abspath(raw_anchor)) + + resolved_target = None + if os.path.exists(candidate_1) and os.path.isfile(candidate_1): + resolved_target = candidate_1 + elif os.path.exists(candidate_2) and os.path.isfile(candidate_2): + resolved_target = candidate_2 + + if resolved_target is None: + raise ValueError( + f"Anchor file '{raw_anchor}' does not exist or is not a regular file within repository '{repo_dir}'." + ) + + # Ensure the resolved file is inside the repository root try: - sys.stdout.reconfigure(encoding="utf-8", errors="replace") - except Exception: - pass + rel_to_repo = os.path.relpath(resolved_target, abs_repo) + except ValueError: + raise ValueError( + f"Anchor file '{raw_anchor}' is located on a different drive than repository '{repo_dir}'." + ) + + # Component-aware traversal check ('..cache/x' is a legitimate name; '../x' is not). + if relpath_escapes_root(rel_to_repo) or os.path.isabs(rel_to_repo): + raise ValueError( + f"Anchor file '{raw_anchor}' resolves outside repository root '{repo_dir}'." + ) + + # Canonical containment (symlinks / junctions), consistent with indexing: an anchor whose + # link target lies outside the repository is rejected, so it cannot name excluded content. + if not is_path_within_root(os.path.realpath(resolved_target), os.path.realpath(abs_repo)): + raise ValueError( + f"Anchor file '{raw_anchor}' is a link that resolves outside repository root '{repo_dir}'." + ) + + clean_rel = rel_to_repo.replace("\\", "/") + if clean_rel not in normalized: + normalized.append(clean_rel) + + return normalized + + +def build_parser() -> argparse.ArgumentParser: + """Builds and returns the argument parser for RepoNeedle CLI.""" parser = argparse.ArgumentParser( prog="reponeedle", description="Token-Budget-Aware, Workflow-Adaptive Repository Context Retrieval for AI Coding Agents.", @@ -64,29 +134,220 @@ def main(): choices=["legacy", "bm25", "arm101", "cascade", "change_impact"], help="Retrieval ranker architecture (default: legacy).", ) + parser.add_argument( + "--json", + action="store_true", + help="Emit results as machine-readable JSON on stdout without terminal formatting or ANSI codes.", + ) + parser.add_argument( + "--anchors", + type=str, + nargs="+", + default=None, + metavar="PATH", + help="One or more seed or anchor file paths for change-impact analysis or ripple queries.", + ) + parser.add_argument( + "--include-context", + action="store_true", + help="Include the full rendered prompt context in the JSON response under 'rendered_context'.", + ) + parser.add_argument( + "--enable-experimental-abstention", + action="store_true", + help=( + "Opt in to the EXPERIMENTAL, uncalibrated out-of-scope abstention heuristic " + "(disabled by default; 0/82 correct abstentions on the ARB v2 selective split). " + "'--task selective' does not enable it." + ), + ) + parser.add_argument( + "--max-file-size-bytes", + type=int, + default=DEFAULT_MAX_FILE_SIZE_BYTES, + metavar="BYTES", + help=( + f"Skip files strictly larger than this many bytes during indexing " + f"(default: {DEFAULT_MAX_FILE_SIZE_BYTES}). Skipped paths are reported in diagnostics." + ), + ) + return parser + - args = parser.parse_args() - console = Console() +def _indexing_notice_lines(diag) -> List[str]: + """Human-readable lines describing indexing exclusions (paths only, never contents).""" + lines: List[str] = [] + if diag.skipped_large_files: + lines.append( + f"{diag.skipped_large_files} file(s) larger than {diag.max_file_size_bytes} bytes were not indexed:" + ) + for p in diag.skipped_paths[:_MAX_SKIPPED_PATHS_SHOWN]: + lines.append(f" - {p}") + remaining = len(diag.skipped_paths) - _MAX_SKIPPED_PATHS_SHOWN + if remaining > 0: + lines.append(f" ... and {remaining} more (use --json for the full list)") + if diag.skipped_unreadable_files: + lines.append(f"{diag.skipped_unreadable_files} file(s) could not be read and were not indexed.") + if diag.skipped_symlink_outside_repository: + lines.append( + f"{diag.skipped_symlink_outside_repository} linked file(s) resolve outside the repository " + f"and were not read (reason: symlink_outside_repository):" + ) + for p in diag.symlink_outside_repository_paths[:_MAX_SKIPPED_PATHS_SHOWN]: + lines.append(f" - {p}") + if diag.max_files_reached: + lines.append(f"Indexing stopped at the max_files limit ({diag.max_files}); remaining files were not indexed.") + return lines - console.print(Panel(f"[bold cyan]RepoNeedle Context Retrieval[/bold cyan]\nTarget Repository: [green]{args.repo}[/green]\nQuery: [yellow]\"{args.query}\"[/yellow]\nToken Budget: [magenta]{args.budget}[/magenta]\nRanker: [cyan]{args.ranker}[/cyan]", expand=False)) + +def main(argv: Optional[List[str]] = None) -> int: + if hasattr(sys.stdout, "reconfigure"): + try: + sys.stdout.reconfigure(encoding="utf-8", errors="replace") + except Exception: + pass + + parser = build_parser() + args = parser.parse_args(argv) + + # Validate repository path + repo_path = os.path.abspath(args.repo) + if not os.path.isdir(repo_path): + sys.stderr.write(f"Error: Repository directory '{args.repo}' does not exist or is not a directory.\n") + return 1 + + # Validate and normalize anchor files + try: + normalized_anchors = resolve_and_validate_anchors(args.repo, args.anchors) + except ValueError as err: + sys.stderr.write(f"Error: {err}\n") + return 1 + + # Validate file-size limit before any indexing work + try: + max_file_size_bytes = validate_max_file_size_bytes(args.max_file_size_bytes) + except (TypeError, ValueError) as err: + sys.stderr.write(f"Error: {err}\n") + return 1 config = BudgetConfig( max_tokens=args.budget, allow_skeletons=not args.no_skeletons, mode=args.mode, + enable_experimental_abstention=args.enable_experimental_abstention, ) - with console.status("[bold green]Indexing repository..."): - engine = RepoNeedleEngine.from_directory(args.repo, budget_config=config) + console = None + if not args.json: + console = Console() + console.print( + Panel( + f"[bold cyan]RepoNeedle Context Retrieval[/bold cyan]\n" + f"Target Repository: [green]{args.repo}[/green]\n" + f"Query: [yellow]\"{args.query}\"[/yellow]\n" + f"Token Budget: [magenta]{args.budget}[/magenta]\n" + f"Ranker: [cyan]{args.ranker}[/cyan]" + + (f"\nAnchors: [blue]{', '.join(normalized_anchors)}[/blue]" if normalized_anchors else "") + + ( + "\nAbstention: [yellow]EXPERIMENTAL heuristic enabled (uncalibrated)[/yellow]" + if args.enable_experimental_abstention else "" + ), + expand=False, + ) + ) + with console.status("[bold green]Indexing repository..."): + engine = RepoNeedleEngine.from_directory( + args.repo, budget_config=config, max_file_size_bytes=max_file_size_bytes + ) + # Indexing exclusions are diagnostics: always routed to stderr, never stdout. + notice = _indexing_notice_lines(engine.indexing_diagnostics) + if notice: + Console(stderr=True).print( + Panel("\n".join(notice), title="Indexing notice", border_style="yellow", expand=False), + markup=False, + ) + else: + # The engine logs a summary WARNING to stderr when files are skipped; the full, + # machine-readable list is emitted in the JSON payload under "indexing". + engine = RepoNeedleEngine.from_directory( + args.repo, budget_config=config, max_file_size_bytes=max_file_size_bytes + ) task_type = TaskType(args.task) if args.task != "auto" else None - result = engine.retrieve(query=args.query, budget_tokens=args.budget, task_type=task_type, ranker=args.ranker) + result = engine.retrieve( + query=args.query, + budget_tokens=args.budget, + task_type=task_type, + ranker=args.ranker, + anchor_files=normalized_anchors, + ) + + # Handle output-file writing if requested + if args.output_file: + try: + with open(args.output_file, "w", encoding="utf-8") as f: + f.write(result.rendered_context) + if not args.json and console: + console.print(f"[bold green]Serialized prompt saved to:[/bold green] {args.output_file}") + except Exception as exc: + sys.stderr.write(f"Error saving output file '{args.output_file}': {exc}\n") + return 1 + + # JSON output branch + if args.json: + selected_list = [] + for rank, sf in enumerate(result.selected_files, start=1): + selected_list.append({ + "rank": rank, + "path": sf.path, + "tier": sf.tier.value if hasattr(sf.tier, "value") else str(sf.tier), + "token_count": int(sf.token_count), + "score": round(float(sf.score), 4), + "explanation": sf.explanation or "", + }) + + payload = { + "schema_version": "1.2.0", + "status": "abstained" if result.abstained else "success", + "query": result.query, + "repository": args.repo, + "ranker": result.ranker or args.ranker, + "task_type": result.task_type.value if hasattr(result.task_type, "value") else str(result.task_type), + "abstained": bool(result.abstained), + "explanation": result.explanation or "", + "budget_tokens": int(result.budget_tokens), + "total_tokens": int(result.total_tokens), + "execution_time_ms": round(float(result.execution_time_ms), 2), + "confidence_score": round(float(result.confidence_score), 4), + "selected_files": selected_list, + "abstention": result.abstention.model_dump() if result.abstention is not None else None, + "indexing": engine.indexing_diagnostics.model_dump(), + } + + if args.include_context or args.serialize: + payload["rendered_context"] = result.rendered_context + print(json.dumps(payload, indent=2, ensure_ascii=False)) + return 0 + + # Rich terminal output branch (existing behavior) if result.abstained: - console.print(Panel(f"[bold red]System Abstained[/bold red]\n{result.explanation}\nConfidence Score: {result.confidence_score:.3f}\nExecution Time: {result.execution_time_ms:.1f}ms", border_style="red")) - return + is_heuristic = result.abstention is not None and result.abstention.status == "evaluated" + heading = "System Abstained (experimental heuristic)" if is_heuristic else "System Abstained" + console.print( + Panel( + f"[bold red]{heading}[/bold red]\n" + f"{result.explanation}\n" + f"Confidence Score: {result.confidence_score:.3f}\n" + f"Execution Time: {result.execution_time_ms:.1f}ms", + border_style="red", + ) + ) + return 0 - table = Table(title=f"Retrieved Context ({result.total_tokens}/{result.budget_tokens} tokens) - {result.execution_time_ms:.1f}ms") + table = Table( + title=f"Retrieved Context ({result.total_tokens}/{result.budget_tokens} tokens) - {result.execution_time_ms:.1f}ms" + ) table.add_column("Rank", justify="center", style="cyan") table.add_column("File Path", style="green") table.add_column("Tier", justify="center", style="magenta") @@ -106,11 +367,6 @@ def main(): console.print(table) - if args.output_file: - with open(args.output_file, "w", encoding="utf-8") as f: - f.write(result.rendered_context) - console.print(f"[bold green]Serialized prompt saved to:[/bold green] {args.output_file}") - if args.serialize: console.print("\n[bold cyan]=== Serialized Context Prompt ===[/bold cyan]\n") try: @@ -119,7 +375,8 @@ def main(): enc = sys.stdout.encoding or "utf-8" print(result.rendered_context.encode(enc, errors="replace").decode(enc)) + return 0 -if __name__ == "__main__": - main() +if __name__ == "__main__": + sys.exit(main()) diff --git a/reponeedle/core/abstention.py b/reponeedle/core/abstention.py index 1665d75..de69c49 100644 --- a/reponeedle/core/abstention.py +++ b/reponeedle/core/abstention.py @@ -1,6 +1,12 @@ """ -Calibrated selective abstention engine for no-gold and out-of-repository queries. -Addresses the selective retrieval calibration gap identified in Agent Retrieval Bench. +EXPERIMENTAL selective abstention heuristic for no-gold and out-of-repository queries. + +Status: experimental, NOT production-calibrated. On the official Agent Retrieval Bench v2 +selective split (N=82 negative samples) it correctly abstained on 0 of 82. Since 1.2.0 the +engine only applies it when explicitly enabled (BudgetConfig.enable_experimental_abstention, +the per-call `enable_experimental_abstention` override, the CLI flag +`--enable-experimental-abstention`, or the MCP retrieve argument of the same name). +See KNOWN_LIMITATIONS.md for details. """ from typing import List, Dict, Set, Tuple @@ -76,7 +82,8 @@ def evaluate_abstention( Evaluates abstention criteria. Returns: abstained (bool): True if system should abstain. - confidence (float): Calibrated confidence score in [0, 1]. + confidence (float): Uncalibrated heuristic score in [0, 1]. It is a diagnostic + signal, not a probability that the repository contains the answer. reason (str): Diagnostic explanation. """ if len(fused_scores) == 0: @@ -114,7 +121,7 @@ def evaluate_abstention( median_score = float(np.median(fused_scores)) margin = max(0.0, max_score - median_score) - # Calibrated composite confidence score + # Composite heuristic confidence score (uncalibrated; weights are fixed, not fitted) confidence = ( 0.30 * z_component + 0.45 * overlap_ratio + diff --git a/reponeedle/engine.py b/reponeedle/engine.py index 2dc061f..1623431 100644 --- a/reponeedle/engine.py +++ b/reponeedle/engine.py @@ -5,6 +5,9 @@ import os import time +import logging +import pathlib +import numbers from typing import List, Optional, Union, Dict, Set import numpy as np import pathspec @@ -19,6 +22,8 @@ BudgetConfig, AblationConfig, ContextTier, + AbstentionDiagnostics, + IndexingDiagnostics, ) from reponeedle.core.function_graph import FunctionGraphEngine from reponeedle.core.arm101 import ARM101Ranker @@ -40,6 +45,58 @@ "package-lock.json", "yarn.lock", "pnpm-lock.yaml", "poetry.lock", ] +# Files strictly larger than this many bytes are excluded from the index (a file exactly at +# the limit is indexed). Exclusions are recorded in RepoNeedleEngine.indexing_diagnostics. +DEFAULT_MAX_FILE_SIZE_BYTES = 500_000 + +logger = logging.getLogger(__name__) + + +def relpath_escapes_root(rel_path: str) -> bool: + """True if a relative path climbs out of its base (first component is '..'). + + Component-aware: names that merely start with '..' (e.g. '..cache/x') do not escape.""" + parts = pathlib.PurePath(rel_path).parts + return bool(parts) and parts[0] == os.pardir + + +def is_path_within_root(path: str, real_root: str) -> bool: + """True if the canonical `path` is `real_root` itself or lies beneath it (component-aware).""" + try: + root_n = os.path.normcase(real_root) + return os.path.commonpath([root_n, os.path.normcase(path)]) == root_n + except ValueError: # different drives / mixed absolute-relative paths + return False + + +def resolve_within_root(path: str, real_root: str) -> Optional[str]: + """ + Resolve `path` to its canonical location (following symlinks and, on Windows, junctions). + + Returns the resolved path if it stays inside `real_root`, or None if it resolves outside. + Raises OSError (or RuntimeError/ValueError) if resolution fails, e.g. broken links or loops. + """ + resolved = os.path.realpath(path, strict=True) + return resolved if is_path_within_root(resolved, real_root) else None + + +def validate_max_file_size_bytes(value) -> int: + """ + Validates a file-size limit. The contract is a strictly positive integer number of bytes. + + Raises: + TypeError: If the value is not an integer (bool is rejected). + ValueError: If the value is zero or negative. + """ + if isinstance(value, bool) or not isinstance(value, numbers.Integral): + raise TypeError( + f"max_file_size_bytes must be a positive integer number of bytes, got {type(value).__name__}." + ) + value = int(value) + if value <= 0: + raise ValueError(f"max_file_size_bytes must be a positive integer (> 0), got {value}.") + return value + class RepoNeedleEngine: """ @@ -86,6 +143,9 @@ def __init__( self.change_impact_config = change_impact_config or ChangeImpactConfig() self.change_impact_ranker = ChangeImpactRanker(self.change_impact_config) + # Populated with scan statistics by from_directory(); "documents" source otherwise. + self.indexing_diagnostics = IndexingDiagnostics(source="documents", indexed_files=0) + if docs: self._fit(docs) @@ -116,6 +176,7 @@ def _fit(self, docs: List[FileDoc]): self.repo_graph.fit(clean_docs) self.function_graph.build_graph(clean_docs) self.abstainer.fit(clean_docs) + self.indexing_diagnostics.indexed_files = len(clean_docs) def update_document(self, doc: FileDoc): """ @@ -154,6 +215,7 @@ def update_documents(self, docs: List[FileDoc]): self.repo_graph.fit(clean_docs) self.function_graph.build_graph(clean_docs) self.abstainer.fit(clean_docs) + self.indexing_diagnostics.indexed_files = len(clean_docs) def query(self, *args, **kwargs) -> RetrievalResult: """Alias for retrieve() to provide intuitive querying API.""" @@ -192,10 +254,20 @@ def from_directory( ranker: Optional[Union[str, RankerType]] = RankerType.LEGACY, ranking_engine: Optional[Union[str, RankerType]] = None, change_impact_config: Optional[ChangeImpactConfig] = None, + max_file_size_bytes: int = DEFAULT_MAX_FILE_SIZE_BYTES, ) -> "RepoNeedleEngine": """ Scans a local repository directory, parses files, and builds the retrieval index. + + Files strictly larger than ``max_file_size_bytes`` (default 500,000) are excluded from + the index. Every exclusion (oversized or unreadable) is recorded by repository-relative + path in ``engine.indexing_diagnostics`` and summarized via a WARNING on the + ``reponeedle.engine`` logger (stderr by default). File contents are never recorded. + + Raises: + TypeError / ValueError: If ``max_file_size_bytes`` is not a positive integer. """ + max_file_size_bytes = validate_max_file_size_bytes(max_file_size_bytes) repo_dir = os.path.abspath(repo_dir) ignore_patterns = list(DEFAULT_IGNORE_PATTERNS) if extra_ignore: @@ -214,6 +286,12 @@ def from_directory( spec = pathspec.PathSpec.from_lines("gitwildmatch", ignore_patterns) docs: List[FileDoc] = [] + skipped_large: List[str] = [] + unreadable: List[str] = [] + outside_root: List[str] = [] + max_files_reached = False + # Canonical repository root: every indexed file must resolve beneath it. + real_root = os.path.realpath(repo_dir) for root, dirs, files in os.walk(repo_dir): # Prune ignored directories @@ -238,13 +316,31 @@ def from_directory( continue full_path = os.path.join(root, file) + # Containment: resolve links BEFORE touching the target, and read only the + # validated canonical path (never the link itself). Links resolving outside the + # repository are skipped and recorded by their in-repository path only. try: - # Skip large files (> 500KB) - if os.path.getsize(full_path) > 500_000: - continue - with open(full_path, "r", encoding="utf-8", errors="ignore") as f: + resolved = resolve_within_root(full_path, real_root) + except (OSError, RuntimeError, ValueError): # broken link, loop, permission error + unreadable.append(rel_path) + continue + if resolved is None: + outside_root.append(rel_path) + continue + try: + file_size = os.path.getsize(resolved) + except OSError: + unreadable.append(rel_path) + continue + # Exclude oversized files (strictly greater than the limit), but record them. + if file_size > max_file_size_bytes: + skipped_large.append(rel_path) + continue + try: + with open(resolved, "r", encoding="utf-8", errors="ignore") as f: content = f.read() except Exception: + unreadable.append(rel_path) continue # Detect if test file @@ -278,11 +374,13 @@ def from_directory( ) if len(docs) >= max_files: + max_files_reached = True break if len(docs) >= max_files: + max_files_reached = True break - return cls( + engine = cls( docs=docs, budget_config=budget_config, ablation_config=ablation_config, @@ -291,6 +389,52 @@ def from_directory( change_impact_config=change_impact_config, ) + skipped_large.sort() + unreadable.sort() + outside_root.sort() + engine.indexing_diagnostics = IndexingDiagnostics( + source="directory", + indexed_files=len(engine.docs), + max_file_size_bytes=max_file_size_bytes, + skipped_large_files=len(skipped_large), + skipped_paths=skipped_large, + skipped_unreadable_files=len(unreadable), + unreadable_paths=unreadable, + skipped_symlink_outside_repository=len(outside_root), + symlink_outside_repository_paths=outside_root, + max_files=max_files, + max_files_reached=max_files_reached, + ) + if outside_root: + logger.warning( + "RepoNeedle skipped %d linked file(s) that resolve outside the repository root " + "(reason: symlink_outside_repository); their targets were not read. See indexing " + "diagnostics (symlink_outside_repository_paths) for the in-repository link paths.", + len(outside_root), + ) + + # Summaries only (counts + limit): no paths or contents in log records. + if skipped_large: + logger.warning( + "RepoNeedle skipped %d file(s) larger than max_file_size_bytes=%d; they are not " + "indexed or searchable. See indexing diagnostics (skipped_paths) for details.", + len(skipped_large), + max_file_size_bytes, + ) + if unreadable: + logger.warning( + "RepoNeedle could not read %d file(s) during indexing. See indexing diagnostics " + "(unreadable_paths) for details.", + len(unreadable), + ) + if max_files_reached: + logger.warning( + "RepoNeedle stopped indexing at max_files=%d; remaining files were not indexed.", + max_files, + ) + + return engine + def retrieve( self, query: Union[str, Query], @@ -300,13 +444,26 @@ def retrieve( ranker: Optional[Union[str, RankerType]] = None, ranking_engine: Optional[Union[str, RankerType]] = None, change_impact_config: Optional[ChangeImpactConfig] = None, + anchor_files: Optional[List[str]] = None, + enable_experimental_abstention: Optional[bool] = None, ) -> RetrievalResult: """ Executes workflow-adaptive retrieval and packs results within the token budget. Supports controlled ablation overrides and modular ranker architectures. + + enable_experimental_abstention: + Per-call override for the experimental, uncalibrated abstention heuristic. + None (default) uses ``budget_config.enable_experimental_abstention`` (False unless + configured). Passing ``task_type=TaskType.SELECTIVE`` does NOT enable abstention. + Empty repositories always return an abstained result regardless of this flag. """ start_time = time.perf_counter() ablation = ablation_config or self.ablation_config or AblationConfig() + if enable_experimental_abstention is None: + abstention_enabled = bool(getattr(self.budget_config, "enable_experimental_abstention", False)) + else: + abstention_enabled = bool(enable_experimental_abstention) + abstention_threshold = float(self.abstainer.confidence_threshold) raw_ranker = ranking_engine if ranking_engine is not None else ranker if raw_ranker is not None: @@ -322,10 +479,19 @@ def retrieve( f"Unknown ranker '{effective_ranker}'. Valid rankers are: 'legacy', 'bm25', 'arm101', 'cascade', 'change_impact'." ) - if isinstance(query, str): + if anchor_files: + norm_anchors = [a.replace("\\", "/") for a in anchor_files] + if isinstance(query, str): + query_obj = Query(text=query, anchor_files=norm_anchors) + else: + query_obj = query + query_obj.anchor_files = norm_anchors + elif isinstance(query, str): query_obj = Query(text=query) else: query_obj = query + if query_obj.anchor_files: + query_obj.anchor_files = [a.replace("\\", "/") for a in query_obj.anchor_files] if task_type: query_obj.task_type = task_type @@ -349,6 +515,12 @@ def retrieve( execution_time_ms=(time.perf_counter() - start_time) * 1000, ranker=effective_ranker, ranked_files=[], + abstention=AbstentionDiagnostics( + enabled=abstention_enabled, + status="empty_repository", + threshold=abstention_threshold, + reason="Repository contains no candidate files (structural; independent of the experimental heuristic).", + ), ) num_docs = len(self.docs) @@ -394,7 +566,13 @@ def retrieve( workflow_boosts = np.zeros(num_docs, dtype=np.float32) elif effective_ranker == "change_impact": - inferred_task = query_obj.task_type or TaskType.COMMENT2CONTEXT + # Restores the behaviour of the published PyPI 1.1.0 wheel: AUTO queries report the + # router-classified task; explicit task types are reported unchanged. Reporting only: + # the change-impact ranker receives `query_obj`, which is not modified here. + if query_obj.task_type == TaskType.AUTO: + inferred_task, _ = self.router.classify_intent(query_obj) + else: + inferred_task = query_obj.task_type ci_cfg = change_impact_config or getattr(self, "change_impact_config", None) or ChangeImpactConfig() ranked_indices, fused_scores, impact_exps = self.change_impact_ranker.rank_with_scores( self.docs, lex_scores, sem_scores, query_obj, config_override=ci_cfg @@ -504,15 +682,37 @@ def retrieve( ranked_paths = [self.docs[i].path for i in ranked_indices] - # 3. Selective Abstention Check - abstained, confidence, reason = self.abstainer.evaluate_abstention( + # 3. Experimental Selective Abstention Check (opt-in only). + # The heuristic score is always computed so `confidence_score` keeps its existing meaning + # (an uncalibrated signal), but it only decides the outcome when explicitly enabled. + heuristic_abstained, confidence, reason = self.abstainer.evaluate_abstention( query=query_obj.text, fused_scores=fused_scores, ) exec_time = (time.perf_counter() - start_time) * 1000 - if abstained: + if abstention_enabled: + abstention_diag = AbstentionDiagnostics( + enabled=True, + status="evaluated", + threshold=abstention_threshold, + reason=reason, + ) + outcome_note = f"Experimental abstention check passed (uncalibrated heuristic): {reason}" + else: + abstention_diag = AbstentionDiagnostics( + enabled=False, + status="disabled", + threshold=abstention_threshold, + reason=( + "Experimental abstention disabled (default); out-of-scope queries are not " + "detected and results are returned regardless of query scope." + ), + ) + outcome_note = "Experimental abstention disabled." + + if abstention_enabled and heuristic_abstained: return RetrievalResult( query=query_obj.text, task_type=inferred_task, @@ -522,10 +722,11 @@ def retrieve( total_tokens=0, budget_tokens=eff_budget, candidate_count=num_docs, - explanation=f"Abstained: {reason}", + explanation=f"Abstained: {reason} (Experimental abstention heuristic; not production-calibrated.)", execution_time_ms=exec_time, ranker=effective_ranker, ranked_files=ranked_paths, + abstention=abstention_diag, ) # 4. Budget-Aware Context Packing (ordered by ranked_indices) @@ -568,9 +769,10 @@ def retrieve( total_tokens=total_tokens_used, budget_tokens=eff_budget, candidate_count=num_docs, - explanation=f"Retrieved {len(selected)} files ({total_tokens_used}/{eff_budget} tokens). Ranker: {effective_ranker}. Task: {inferred_task.value}. {reason}", + explanation=f"Retrieved {len(selected)} files ({total_tokens_used}/{eff_budget} tokens). Ranker: {effective_ranker}. Task: {inferred_task.value}. {outcome_note}", execution_time_ms=(time.perf_counter() - start_time) * 1000, ranker=effective_ranker, ranked_files=ranked_paths, rendered_context=rendered_context, + abstention=abstention_diag, ) diff --git a/reponeedle/mcp/__init__.py b/reponeedle/mcp/__init__.py new file mode 100644 index 0000000..ee8bae9 --- /dev/null +++ b/reponeedle/mcp/__init__.py @@ -0,0 +1,9 @@ +""" +RepoNeedle Model Context Protocol (MCP) Server. +Provides local STDIO context retrieval tools for AI coding agents. +""" + +__all__ = ["create_mcp_server", "main", "MCPServerConfig", "validate_and_normalize_path"] + +from reponeedle.mcp.config import MCPServerConfig, validate_and_normalize_path +from reponeedle.mcp.server import create_mcp_server, main diff --git a/reponeedle/mcp/__main__.py b/reponeedle/mcp/__main__.py new file mode 100644 index 0000000..68ee031 --- /dev/null +++ b/reponeedle/mcp/__main__.py @@ -0,0 +1,9 @@ +""" +Execution wrapper for `python -m reponeedle.mcp`. +""" + +import sys +from reponeedle.mcp.server import main + +if __name__ == "__main__": + sys.exit(main()) diff --git a/reponeedle/mcp/config.py b/reponeedle/mcp/config.py new file mode 100644 index 0000000..383c376 --- /dev/null +++ b/reponeedle/mcp/config.py @@ -0,0 +1,69 @@ +""" +Configuration and security path validation for RepoNeedle MCP Server. +""" + +import os +from pathlib import Path +from typing import Optional +from pydantic import BaseModel, Field + +from reponeedle.models import BudgetConfig +from reponeedle.engine import relpath_escapes_root + + +class MCPServerConfig(BaseModel): + """Configuration schema for RepoNeedle MCP Server.""" + repo_dir: str = Field(default=".", description="Path to repository root.") + budget: int = Field(default=4000, description="Default token budget for context packing.") + allow_skeletons: bool = Field(default=True, description="Allow structural skeletons in packed context.") + mode: str = Field(default="auto", description="CUD-Pack delivery mode ('auto', 'compact', 'complete').") + max_files: int = Field(default=3000, description="Maximum files to index.") + max_file_size_bytes: int = Field( + default=500_000, + gt=0, + description="Files strictly larger than this many bytes are skipped (and reported) during indexing.", + ) + + +def validate_and_normalize_path(repo_dir: str, target_path: str) -> str: + """ + Validates a file path against the configured repository root. + Enforces path traversal protection and symlink escape checks. + Returns normalized repository-relative path with forward slashes. + + Raises: + ValueError: If path is empty, does not exist, is not a file, or violates security bounds. + """ + if not target_path or not str(target_path).strip(): + raise ValueError("File path cannot be empty.") + + raw = str(target_path).strip() + abs_repo = os.path.realpath(os.path.abspath(repo_dir)) + + # Resolve candidate relative to repo_dir or current working directory + cand1 = os.path.normpath(os.path.join(abs_repo, raw)) + cand2 = os.path.normpath(os.path.abspath(raw)) + + chosen = None + if os.path.exists(cand1) and os.path.isfile(cand1): + chosen = cand1 + elif os.path.exists(cand2) and os.path.isfile(cand2): + chosen = cand2 + + if chosen is None: + raise ValueError(f"Path '{raw}' does not exist or is not a regular file within repository '{repo_dir}'.") + + # Canonicalize symlinks + real_chosen = os.path.realpath(chosen) + + # Verify target stays strictly within real_repo + try: + rel = os.path.relpath(real_chosen, abs_repo) + except ValueError: + raise ValueError(f"Path '{raw}' is located on a different drive than repository '{repo_dir}'.") + + # Component-aware: a legitimate name such as '..cache/x' is not a traversal. + if relpath_escapes_root(rel) or os.path.isabs(rel): + raise ValueError(f"Security boundary violation: Path '{raw}' resolves outside repository root '{repo_dir}'.") + + return rel.replace("\\", "/") diff --git a/reponeedle/mcp/server.py b/reponeedle/mcp/server.py new file mode 100644 index 0000000..ad73d44 --- /dev/null +++ b/reponeedle/mcp/server.py @@ -0,0 +1,215 @@ +""" +Server entrypoint and factory for RepoNeedle Native MCP Server. +""" + +import os +import sys +import time +import argparse +from typing import Optional, List + +try: + from mcp.server.fastmcp import FastMCP +except ImportError: + FastMCP = None + +from reponeedle.engine import ( + RepoNeedleEngine, + DEFAULT_MAX_FILE_SIZE_BYTES, + validate_max_file_size_bytes, +) +from reponeedle.models import BudgetConfig +from reponeedle.mcp.tools import register_tools + + +def create_mcp_server( + repo_dir: str = ".", + budget: int = 4000, + allow_skeletons: bool = True, + mode: str = "auto", + max_file_size_bytes: int = DEFAULT_MAX_FILE_SIZE_BYTES, +) -> "FastMCP": + """ + Factory function creating and configuring a RepoNeedle FastMCP server instance. + Indexes the repository once at startup and reuses the in-memory engine across tool calls. + """ + if FastMCP is None: + raise ImportError( + "The RepoNeedle MCP server requires the 'mcp' extra. " + "Install it via: pip install 'reponeedle[mcp]'" + ) + + max_file_size_bytes = validate_max_file_size_bytes(max_file_size_bytes) + abs_repo = os.path.realpath(os.path.abspath(repo_dir)) + if not os.path.isdir(abs_repo): + raise ValueError(f"Repository directory '{repo_dir}' does not exist or is not a directory.") + + sys.stderr.write(f"[reponeedle-mcp] Indexing repository: {abs_repo} ...\n") + sys.stderr.flush() + + start_time = time.perf_counter() + config = BudgetConfig( + max_tokens=budget, + allow_skeletons=allow_skeletons, + mode=mode, + ) + engine = RepoNeedleEngine.from_directory( + abs_repo, budget_config=config, max_file_size_bytes=max_file_size_bytes + ) + init_time_ms = (time.perf_counter() - start_time) * 1000 + + sys.stderr.write( + f"[reponeedle-mcp] Repository indexed successfully ({len(engine.docs)} files in {init_time_ms:.1f}ms).\n" + ) + diag = engine.indexing_diagnostics + if (diag.skipped_large_files or diag.skipped_unreadable_files or diag.max_files_reached + or diag.skipped_symlink_outside_repository): + sys.stderr.write( + f"[reponeedle-mcp] Indexing exclusions: {diag.skipped_large_files} file(s) over " + f"{diag.max_file_size_bytes} bytes, {diag.skipped_unreadable_files} unreadable" + + (f", {diag.skipped_symlink_outside_repository} linked outside the repository (not read)" + if diag.skipped_symlink_outside_repository else "") + + (f", stopped at max_files={diag.max_files}" if diag.max_files_reached else "") + + ". Call reponeedle_inspect for the skipped paths.\n" + ) + sys.stderr.flush() + + app = FastMCP( + name="reponeedle", + instructions=( + "RepoNeedle is a CPU-native, token-budget-aware repository context retrieval engine. " + "Use reponeedle_retrieve to find code for tasks, reponeedle_change_impact to assess refactoring risks, " + "and reponeedle_inspect to view repository index metadata." + ), + ) + + register_tools(app=app, engine=engine, repo_dir=abs_repo, init_time_ms=init_time_ms) + _set_server_version(app) + return app + + +def _set_server_version(app) -> None: + """ + Report RepoNeedle's package version as MCP `serverInfo.version`. + + FastMCP does not accept a version argument; its low-level server falls back to the MCP SDK's + own version when none is set. Only `serverInfo.version` changes; the negotiated protocol version + and the SDK dependency are unaffected. + """ + from reponeedle import __version__ + low_level = getattr(app, "_mcp_server", None) + if low_level is not None and hasattr(low_level, "version"): + low_level.version = __version__ + + +def build_parser() -> argparse.ArgumentParser: + """Builds and returns CLI argument parser for the MCP server.""" + parser = argparse.ArgumentParser( + prog="reponeedle-mcp", + description="Local Model Context Protocol (MCP) server for RepoNeedle repository retrieval.", + ) + parser.add_argument( + "--repo", + "-r", + type=str, + default=".", + help="Path to repository root (default: current directory).", + ) + parser.add_argument( + "--budget", + "-b", + type=int, + default=4000, + help="Default context token budget (default: 4000).", + ) + parser.add_argument( + "--transport", + type=str, + default="stdio", + choices=["stdio"], + help="MCP transport protocol (default: stdio).", + ) + parser.add_argument( + "--no-skeletons", + action="store_true", + help="Disallow structural skeleton mode; require full files.", + ) + parser.add_argument( + "--max-file-size-bytes", + type=int, + default=DEFAULT_MAX_FILE_SIZE_BYTES, + metavar="BYTES", + help=( + f"Skip files strictly larger than this many bytes during indexing " + f"(default: {DEFAULT_MAX_FILE_SIZE_BYTES}). Skipped paths are reported by reponeedle_inspect." + ), + ) + return parser + + +def main(argv: Optional[List[str]] = None) -> int: + """ + Main entrypoint for reponeedle-mcp executable. + Communicates via STDIO. All diagnostic logs are sent to STDERR. + """ + if hasattr(sys.stdout, "reconfigure"): + try: + sys.stdout.reconfigure(encoding="utf-8", errors="replace") + except Exception: + pass + + if FastMCP is None: + sys.stderr.write( + "Error: The 'mcp' extra is not installed.\n" + "Please install it using:\n" + " pip install 'reponeedle[mcp]'\n" + ) + sys.stderr.flush() + return 1 + + parser = build_parser() + args = parser.parse_args(argv) + + abs_repo = os.path.realpath(os.path.abspath(args.repo)) + if not os.path.isdir(abs_repo): + sys.stderr.write(f"Error: Repository directory '{args.repo}' does not exist or is not a directory.\n") + sys.stderr.flush() + return 1 + + try: + validate_max_file_size_bytes(args.max_file_size_bytes) + except (TypeError, ValueError) as exc: + sys.stderr.write(f"Error: {exc}\n") + sys.stderr.flush() + return 1 + + try: + app = create_mcp_server( + repo_dir=abs_repo, + budget=args.budget, + allow_skeletons=not args.no_skeletons, + max_file_size_bytes=args.max_file_size_bytes, + ) + except Exception as exc: + sys.stderr.write(f"Fatal error initializing RepoNeedle MCP server: {exc}\n") + sys.stderr.flush() + return 1 + + sys.stderr.write("[reponeedle-mcp] Server ready. Listening for JSON-RPC messages on STDIO...\n") + sys.stderr.flush() + + try: + app.run(transport="stdio") + return 0 + except (KeyboardInterrupt, SystemExit): + sys.stderr.write("[reponeedle-mcp] Server shut down gracefully.\n") + sys.stderr.flush() + return 0 + except Exception as exc: + sys.stderr.write(f"[reponeedle-mcp] Unhandled server error: {exc}\n") + sys.stderr.flush() + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/reponeedle/mcp/tools.py b/reponeedle/mcp/tools.py new file mode 100644 index 0000000..26d6cd0 --- /dev/null +++ b/reponeedle/mcp/tools.py @@ -0,0 +1,226 @@ +""" +Tool definitions and handlers for RepoNeedle MCP Server. +""" + +import time +from typing import List, Optional, Dict, Any, TYPE_CHECKING + +# Type-only import: keeps `reponeedle.mcp` importable without the optional 'mcp' extra so the +# server entrypoint can print its friendly "install reponeedle[mcp]" error instead of crashing. +if TYPE_CHECKING: + from mcp.server.fastmcp import FastMCP + +from reponeedle.engine import RepoNeedleEngine +from reponeedle.models import TaskType, RankerType +from reponeedle.mcp.config import validate_and_normalize_path + +VALID_RANKERS = {"legacy", "bm25", "arm101", "cascade", "change_impact"} +VALID_TASK_TYPES = {"auto", "code2test", "comment2context", "trace2code", "edit2ripple", "selective"} + + +def register_tools( + app: "FastMCP", + engine: RepoNeedleEngine, + repo_dir: str, + init_time_ms: float = 0.0, +) -> None: + """ + Registers the three primary RepoNeedle retrieval and inspection tools + on the provided FastMCP application instance. + """ + + @app.tool( + name="reponeedle_retrieve", + description=( + "Retrieve the most relevant repository context for a natural-language coding task, " + "issue description, or error traceback strictly within the requested token budget." + ), + ) + async def reponeedle_retrieve( + query: str, + budget_tokens: int = 4000, + ranker: str = "legacy", + task_type: str = "auto", + anchor_files: Optional[List[str]] = None, + enable_experimental_abstention: bool = False, + ) -> Dict[str, Any]: + """ + Retrieves context files and rendered markdown prompt within token limits. + + Args: + query: Natural language coding task, issue description, or error traceback. + budget_tokens: Maximum token budget for packed context (default: 4000). + ranker: Ranking engine ('legacy', 'bm25', 'arm101', 'cascade', 'change_impact'). + task_type: Workflow task type ('auto', 'code2test', 'comment2context', 'trace2code', 'edit2ripple', 'selective'). + 'selective' does not enable abstention by itself. + anchor_files: Optional list of seed or modified file paths. + enable_experimental_abstention: Opt in to the EXPERIMENTAL, uncalibrated out-of-scope + abstention heuristic (default: false). When false, results are always returned + (except for an empty repository) and out-of-scope queries are not detected. + """ + if not query or not query.strip(): + raise ValueError("Query string cannot be empty.") + + ranker_clean = ranker.lower().strip() + if ranker_clean not in VALID_RANKERS: + raise ValueError( + f"Unknown ranker '{ranker}'. Supported rankers are: {sorted(list(VALID_RANKERS))}." + ) + + task_type_clean = task_type.lower().strip() + if task_type_clean not in VALID_TASK_TYPES: + raise ValueError( + f"Unknown task_type '{task_type}'. Supported task types are: {sorted(list(VALID_TASK_TYPES))}." + ) + + validated_anchors = [] + if anchor_files: + for a in anchor_files: + validated_anchors.append(validate_and_normalize_path(repo_dir, a)) + + effective_task = TaskType(task_type_clean) if task_type_clean != "auto" else None + result = engine.retrieve( + query=query.strip(), + budget_tokens=budget_tokens, + task_type=effective_task, + ranker=ranker_clean, + anchor_files=validated_anchors, + enable_experimental_abstention=bool(enable_experimental_abstention), + ) + + selected_files_list = [] + for rank, sf in enumerate(result.selected_files, start=1): + selected_files_list.append({ + "rank": rank, + "path": sf.path, + "tier": sf.tier.value if hasattr(sf.tier, "value") else str(sf.tier), + "token_count": int(sf.token_count), + "score": round(float(sf.score), 4), + "explanation": sf.explanation or "", + }) + + return { + "status": "abstained" if result.abstained else "success", + "query": result.query, + "ranker": result.ranker or ranker_clean, + "task_type": result.task_type.value if hasattr(result.task_type, "value") else str(result.task_type), + "abstained": bool(result.abstained), + "explanation": result.explanation or "", + "budget_tokens": int(result.budget_tokens), + "total_tokens": int(result.total_tokens), + "execution_time_ms": round(float(result.execution_time_ms), 2), + "confidence_score": round(float(result.confidence_score), 4), + "selected_files": selected_files_list, + "rendered_context": result.rendered_context if not result.abstained else "", + "abstention": result.abstention.model_dump() if result.abstention is not None else None, + } + + @app.tool( + name="reponeedle_change_impact", + description=( + "Identify files and regression tests potentially affected by modifications to specific " + "anchor files using AST change propagation and Seed File Suppression (SFS)." + ), + ) + async def reponeedle_change_impact( + anchor_files: List[str], + query: str = "Assess change impact", + budget_tokens: int = 4000, + ) -> Dict[str, Any]: + """ + Analyzes change impact and regression risk for one or more modified anchor files. + + Args: + anchor_files: List of modified file paths to analyze (at least one required). + query: Optional task or bug description (default: 'Assess change impact'). + budget_tokens: Maximum token budget for packed context (default: 4000). + """ + if not anchor_files: + raise ValueError("At least one anchor file is required for change-impact analysis.") + + validated_anchors = [] + for a in anchor_files: + validated_anchors.append(validate_and_normalize_path(repo_dir, a)) + + effective_query = query.strip() if query and query.strip() else "Assess change impact" + result = engine.retrieve( + query=effective_query, + budget_tokens=budget_tokens, + ranker="change_impact", + anchor_files=validated_anchors, + # Change-impact always reports "success"; never let the heuristic empty it silently. + enable_experimental_abstention=False, + ) + + affected_files_list = [] + for rank, sf in enumerate(result.selected_files, start=1): + affected_files_list.append({ + "rank": rank, + "path": sf.path, + "tier": sf.tier.value if hasattr(sf.tier, "value") else str(sf.tier), + "token_count": int(sf.token_count), + "score": round(float(sf.score), 4), + "explanation": sf.explanation or "", + }) + + return { + "status": "success", + "query": result.query, + "anchor_files": validated_anchors, + "budget_tokens": int(result.budget_tokens), + "total_tokens": int(result.total_tokens), + "execution_time_ms": round(float(result.execution_time_ms), 2), + "explanation": result.explanation or "", + "affected_files": affected_files_list, + "rendered_context": result.rendered_context, + } + + @app.tool( + name="reponeedle_inspect", + description=( + "Inspect the indexed repository state: returns file count, supported extensions, " + "sample indexed paths, active rankers, engine configuration, indexing diagnostics " + "(files skipped for exceeding the size limit or being unreadable), and the " + "experimental abstention status." + ), + ) + async def reponeedle_inspect() -> Dict[str, Any]: + """ + Returns diagnostic information about the indexed repository and active retrieval configuration. + """ + indexed_paths = [doc.path for doc in engine.docs] + sample_paths = indexed_paths[:25] + + return { + "status": "ready", + "repository_root": repo_dir, + "indexed_file_count": len(engine.docs), + "supported_file_types": [ + ".py", ".ts", ".js", ".tsx", ".jsx", + ".go", ".rs", ".java", ".c", ".cpp", + ".h", ".md", ".toml", ".yaml", ".yml", ".json" + ], + "indexed_path_examples": sample_paths, + "sample_count": len(sample_paths), + "active_rankers": sorted(list(VALID_RANKERS)), + "default_ranker": "legacy", + "budget_config": { + "max_tokens": engine.budget_config.max_tokens, + "allow_skeletons": engine.budget_config.allow_skeletons, + "mode": engine.budget_config.mode, + }, + "index_initialization": { + "status": "initialized", + "init_time_ms": round(init_time_ms, 2), + "message": "Repository indexed in-memory at server startup. Restart server to refresh index.", + }, + # Paths and counts only; file contents are never included. + "indexing": engine.indexing_diagnostics.model_dump(), + "abstention": { + "default_enabled": bool(getattr(engine.budget_config, "enable_experimental_abstention", False)), + "experimental": True, + "calibrated": False, + "threshold": float(engine.budget_config.abstention_threshold), + "opt_in": "Pass enable_experimental_abstention=true to reponeedle_retrieve.", + }, + } diff --git a/reponeedle/models.py b/reponeedle/models.py index 764ded2..2778b40 100644 --- a/reponeedle/models.py +++ b/reponeedle/models.py @@ -67,6 +67,54 @@ class ScoredFile(BaseModel): explanation: str = "" +class AbstentionDiagnostics(BaseModel): + """ + Describes how (and whether) the experimental abstention heuristic influenced a retrieval. + + status values: + "disabled" - Heuristic not applied (default). Results are returned regardless of + whether the query is in scope for the repository. + "evaluated" - Caller opted in; the heuristic decided whether to abstain. + "empty_repository" - No candidate files exist; the result is abstained structurally, + independent of the heuristic and of the opt-in flag. + + The heuristic is experimental and NOT production-calibrated (ARB v2 selective split: + 0 of 82 negative samples correctly abstained). `experimental` and `calibrated` are + reported on every result so downstream consumers never mistake it for a calibrated signal. + """ + enabled: bool = False + status: str = "disabled" + experimental: bool = True + calibrated: bool = False + threshold: float = 0.12 + reason: str = "" + + +class IndexingDiagnostics(BaseModel): + """ + Records what was (and was not) indexed. Never stores file contents: only + repository-relative paths and counts. + + source: + "directory" - built by RepoNeedleEngine.from_directory (scan statistics are populated). + "documents" - built from in-memory FileDoc objects (no scan was performed). + """ + source: str = "documents" + indexed_files: int = 0 + max_file_size_bytes: Optional[int] = None + skipped_large_files: int = 0 + skipped_paths: List[str] = Field(default_factory=list) + skipped_unreadable_files: int = 0 + unreadable_paths: List[str] = Field(default_factory=list) + # Files (symlinks or, on Windows, junction members) whose canonical location is outside the + # repository root. Reason code: "symlink_outside_repository". Only the in-repository link path + # is recorded; the target path and contents are never read or stored. + skipped_symlink_outside_repository: int = 0 + symlink_outside_repository_paths: List[str] = Field(default_factory=list) + max_files: Optional[int] = None + max_files_reached: bool = False + + class RetrievalResult(BaseModel): query: str task_type: TaskType @@ -81,6 +129,8 @@ class RetrievalResult(BaseModel): ranker: Optional[str] = "legacy" ranked_files: Optional[List[str]] = None rendered_context: str = "" + # Added in 1.2.0 (optional; None for results built outside RepoNeedleEngine.retrieve). + abstention: Optional[AbstentionDiagnostics] = None @property def packed_files(self) -> List[ScoredFile]: @@ -94,6 +144,11 @@ class BudgetConfig(BaseModel): skeleton_token_cap: int = 350 density_exponent: float = 0.75 abstention_threshold: float = 0.12 + # Experimental, uncalibrated out-of-scope abstention heuristic. Disabled by default since 1.2.0. + # When False, retrieval never abstains on heuristic grounds (empty repositories still abstain). + # Selecting TaskType.SELECTIVE does NOT enable it; this flag (or the per-call override on + # RepoNeedleEngine.retrieve) is the only opt-in. + enable_experimental_abstention: bool = False mode: str = "auto" # "auto", "compact", "complete" capacity_elasticity: Optional[float] = None candidate_pool_cutoff: int = 85 diff --git a/reports/arb_v2_current_engine/SUMMARY.json b/reports/arb_v2_current_engine/SUMMARY.json new file mode 100644 index 0000000..e797603 --- /dev/null +++ b/reports/arb_v2_current_engine/SUMMARY.json @@ -0,0 +1,338 @@ +{ + "n_samples": 345, + "n_expected": 345, + "engine_versions": [ + "1.1.0" + ], + "abstained_count": 0, + "overall": { + "reponeedle_current": { + "mrr": 0.21532780783702646, + "recall@5": 0.3032367149758454, + "recall@10": 0.36772946859903377, + "recall@20": 0.38415458937198066, + "bcy@4k": 0.23995169082125603, + 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0000000..f6c74e0 --- /dev/null +++ b/reports/experimental/phase0_evaluator_audit/PHASE0_REPORT.md @@ -0,0 +1,186 @@ +# Phase 0 — ARB v2 Evaluator Forensic Audit + +**Status**: EXPERIMENTAL DIAGNOSTIC (2026-11-09). Development-branch engine (identical to released 1.1.0). Not an official benchmark result; the official harnesses (`benchmarks/run_complete_arb_evaluation.py`, `benchmarks/run_arb_current_engine.py`) and ARB metric definitions are unchanged. +**Tool**: `benchmarks/run_arb_evaluator_audit.py`. **Data**: ARB v2 positive split, N=345 (80 `comment2context`). +**Fidelity**: the audit's recomputed official sequence reproduces the official current-engine run on **345 / 345** queries (MRR, Recall@5/10/20). Its BM25 reproduces the historical official BM25 per-query metrics and BCY@8k on **345 / 345**. + +--- + +## 1. Evaluator execution flow + +| Step | What happens | Code | +| :--- | :--- | :--- | +| Documents | One document per path, built from the **single longest ARB chunk** for that path, truncated to 25,000 chars | `run_complete_arb_evaluation.py` / `run_arb_current_engine.py` | +| Ranking | `engine.retrieve(task_type=, budget=8000)` fuses lexical + semantic (+ workflow boost) scores over **all** files | `reponeedle/engine.py` | +| Packing | CUD-Pack receives the complete ranking, scans the top 85 in rank order, skips files no tier fits, then backfills outlines | `reponeedle/core/packer.py` | +| Evaluator input | `selected_files` (packed order), then **every other file in corpus (chunk-file) order** | harness | +| Metrics | ARB `sample_metrics` → `unique_ranked_paths` → file-level `reciprocal_rank`, `recall_at(k)`; BCY via `pack_files(top-20)` | `agent_retrieval_bench.baseline`, `.bcy_curve` | +| BM25 baseline | **All chunks** ranked by BM25 score (ties by path), collapsed to files | `rank_chunks_bm25_with_scores` | + +The benchmark passes the task label explicitly, so automatic intent classification is **not** exercised by the official numbers. + +## 2. Ranking vs packing (overall, N=345) + +| View | MRR | Recall@5 | Recall@10 | Recall@20 | +| :--- | :---: | :---: | :---: | :---: | +| Official sequence (packed + corpus order) | 0.2153 | 0.3032 | 0.3677 | 0.3842 | +| **View A: RepoNeedle full ranking** | **0.2379** | **0.3061** | **0.4407** | **0.5803** | +| View A: BM25 full ranking (official baseline) | 0.1520 | 0.1899 | 0.3034 | 0.4452 | + +- Full ranking minus official: MRR +0.0226 (repo-clustered 95% CI [+0.0169, +0.0284]); Recall@20 +0.1961 (CI [+0.1188, +0.2521]). +- **96** gold files ranked in RepoNeedle's top 20 are pushed out of the official top 20 by corpus ordering; corpus order "gains" **1**. +- CUD-Pack delivers 8.9 files on average (99.6% budget utilisation). Gold coverage of the packed context is 38.3%; 45.8% of queries get at least one gold file packed. +- ARB's own `pack_files` applied to RepoNeedle's full ranking gives BCY@8k 0.3501 vs 0.3443 for the official sequence. **Packing is not the bottleneck**; ranking is. + +## 3. BM25 vs RepoNeedle under all three views + +| Task (N) | Official MRR / R@20 | **Full-ranking MRR / R@20** | BM25 MRR / R@20 | Packed gold coverage / success | BCY@8k RN-full / BM25 | +| :--- | :---: | :---: | :---: | :---: | :---: | +| code2test (106) | 0.2546 / 0.5176 | **0.2736 / 0.6434** | 0.1057 / 0.3066 | 51.8% / 58.5% | 0.4909 / 0.1541 | +| comment2context (80) | 0.1658 / 0.1938 | **0.1857 / 0.3146** | 0.1974 / 0.5292 | 19.4% / 32.5% | 0.1896 / 0.2604 | +| trace2code (101) | 0.2262 / 0.3548 | **0.2577 / 0.6782** | 0.1638 / 0.4934 | 35.5% / 37.6% | 0.2954 / 0.1931 | +| edit2ripple (58) | 0.1929 / 0.4540 | **0.2104 / 0.6609** | 0.1536 / 0.4986 | 44.5% / 55.2% | 0.4095 / 0.2428 | + +Paired, repository-clustered 95% CIs (full ranking minus BM25): + +| Task | Δ MRR [CI] | Δ Recall@20 [CI] | +| :--- | :---: | :---: | +| Overall | +0.0859 [+0.0372, +0.1186] | +0.1351 [+0.0178, +0.2185] | +| code2test | +0.1679 [+0.1070, +0.2240] | +0.3368 [+0.1987, +0.4642] | +| **comment2context** | **−0.0117 [−0.0991, +0.0635]** (not significant) | **−0.2146 [−0.3720, −0.1011]** (significant) | +| trace2code | +0.0939 [−0.0333, +0.1569] | +0.1848 [−0.0296, +0.2958] | +| edit2ripple | +0.0567 [+0.0179, +0.1194] | +0.1624 [+0.0351, +0.3268] | + +View C (remainder): no gold file has a zero fused or zero BM25 score; every remainder position in the official sequence is a corpus-order position, not a retrieval rank. + +## 4. Representative comment2context traces + +Full traces (top-25 with fused / lexical / semantic / structural scores, packed files with tiers, official top-25 with source, BM25 top-25): `traces.jsonl`. + +| Query | Gold rank: official / full / BM25 | What the trace shows | +| :--- | :---: | :--- | +| `a467dd60…` ipython | 336 / **14** / 60 | Official sequence loses a top-20 hit: after 9 packed files, ranks 10+ are `.editorconfig`, `.flake8`, … in corpus order. Top-2 RepoNeedle results are `CONTRIBUTING.md` and `version6.rst`. | +| `bf473089…` hypothesis | 116 / 335 / **3** | Genuine RepoNeedle ranking failure: tests and `.rst`/`.md` docs fill the top; `core.py` (BM25 rank 3) is at full rank 335. | +| `7d661ed7…` diffusers | **2** / **2** / 150 | RepoNeedle wins clearly; gold packed at position 2. | +| `3e3c5349…` tox | 161 / 108 / 76 | Both fail; RepoNeedle's top 10 are almost all `docs/*.rst`. | + +In every trace the structural (graph) score is 0.000 for every file. + +## 5. Root-cause assessment (comment2context) + +| Candidate cause | Verdict | Evidence | +| :--- | :--- | :--- | +| Evaluator ordering / metric construction | **Major contributor (inflates the deficit)** | Recall@20 0.1938 official → 0.3146 full ranking; 17 lost top-20 hits; MRR deficit vs BM25 not significant on the full ranking | +| CUD-Pack selection | Not a cause | ARB packing of the same ranking gives identical BCY@8k (0.1896) | +| Graph signal dilution | Not a cause | Structural score is 0 for all files (PageRank fusion is off by default); the 0.20 structural weight only rescales scores | +| Incorrect task routing | Not a cause **in the benchmark** (label is passed explicitly). Relevant for real AUTO use: AUTO would route only 55/80 comment2context and **125/345 overall (36%)** to the labelled task | +| Semantic noise | **Not supported** | Semantic-only Recall@20 0.3896 / MRR 0.1942 is *better* than lexical-only (0.2875 / 0.1587) and than the fusion (0.3146 / 0.1857) | +| Lexical signal quality | **Main ranking weakness** | RepoNeedle's own BM25 index scores far below ARB's chunk-level BM25 (Recall@20 0.2875 vs 0.5292) | +| Documentation distractors | **Contributor** | 23% of RepoNeedle's comment2context top-8 are docs vs 7% for BM25; 0% of gold files are docs. `enable_doc_filter` exists but is off by default | +| Query form | **Premise mismatch** | All 345 ARB queries (all tasks) are structured JSON (PR review context, PR descriptions, CI failure excerpts, diffs), none conversational | + +## 5a. Document representation (comment2context, N=80, all queries accounted for) + +**Finding.** The benchmark harness builds each RepoNeedle document from the **single longest ARB chunk** for a path. ARB stores one `file` chunk per path (the file's opening text, truncated; about 2,000 characters in this corpus) plus one chunk per symbol covering the rest of the file. RepoNeedle therefore ranks files from **fragments**, while the official BM25 baseline ranks **every chunk of every file**. Real RepoNeedle use (`from_directory`) reads whole files up to 500 KB, so the official numbers measure a handicapped input condition, not the product. + +**Diagnostic.** `--doc-mode reconstructed` rebuilds each file from its ARB chunks (header lines of the `file` chunk before the first symbol, then all symbol chunks in line order) and re-ranks with the **unchanged** engine. ARB's own BM25 was also run on whole-file documents of both representations, to separate granularity (chunks vs files) from text coverage. + +| comment2context (N=80) | MRR | Recall@5 | Recall@10 | Recall@20 | +| :--- | :---: | :---: | :---: | :---: | +| RepoNeedle full ranking, official documents (longest chunk) | 0.1857 | 0.1646 | 0.2250 | 0.3146 | +| **RepoNeedle full ranking, reconstructed documents** | **0.2037** | **0.2458** | **0.3521** | **0.4896** | +| RepoNeedle official sequence, reconstructed documents | 0.1869 | 0.2458 | 0.3271 | 0.3583 | +| RepoNeedle lexical-only: official → reconstructed | 0.1587 → 0.2071 | 0.1479 → 0.2583 | 0.2375 → 0.3646 | 0.2875 → 0.4646 | +| RepoNeedle semantic-only: official → reconstructed | 0.1942 → 0.1917 | 0.1646 → 0.1833 | 0.2437 → 0.3042 | 0.3896 → 0.4417 | +| ARB BM25 on chunks (official baseline) | 0.1974 | 0.2146 | 0.4042 | 0.5292 | +| ARB BM25 on whole files, longest chunk only | 0.1875 | 0.1875 | 0.2542 | 0.3563 | +| ARB BM25 on whole files, reconstructed | 0.2063 | 0.2812 | 0.4333 | 0.5792 | + +Repository-clustered 95% CIs: + +| Comparison | Δ MRR | Δ Recall@20 | +| :--- | :---: | :---: | +| RepoNeedle reconstructed − RepoNeedle official documents | +0.0180 [−0.0340, +0.0781] | **+0.1750 [+0.0928, +0.2929]** | +| RepoNeedle lexical reconstructed − lexical official | +0.0484 [−0.0119, +0.1188] | **+0.1771 [+0.0968, +0.2981]** | +| **RepoNeedle reconstructed − BM25 (official baseline)** | **+0.0063 [−0.0485, +0.0571]** | **−0.0396 [−0.1945, +0.1099]** | + +Packed context (CUD-Pack, unchanged): gold coverage 19.4% → 35.8%; queries with at least one gold file packed 32.5% → 51.2%. + +Conclusions: +- Text coverage, not ranking logic, is the largest driver. BM25 degrades the same way (Recall@20 0.5792 → 0.3563) when restricted to the fragment RepoNeedle receives. +- With whole-file input, the comment2context difference between RepoNeedle and BM25 is **not statistically significant** on MRR or Recall@20. +- Granularity is not BM25's advantage: whole-file BM25 (0.5792) is at least as good as chunk-level BM25 (0.5292). +- A **residual lexical gap** remains on equal input: RepoNeedle lexical-only Recall@20 0.4646 vs ARB whole-file BM25 0.5792. +- The evaluator gap persists with whole files (official sequence Recall@20 0.3583 vs full ranking 0.4896). + +Reconstructed-document traces (`reconstructed_docs/traces.jsonl`): + +| Query | Gold full rank: fragments → whole files | Note | +| :--- | :---: | :--- | +| `bf473089…` hypothesis | **335 → 2** (and 513 → 28) | Fixed by representation; gold now packed | +| `3e3c5349…` tox | 108 → 36 | Improves, but the top 5 are all `docs/*.rst` | +| `a467dd60…` ipython | 14 → 12 | `CONTRIBUTING.md`, `version6.rst` still in top 5 | +| `7d661ed7…` diffusers | 2 → **9** | Worse: two `contribution.md` files enter the top 5; documentation gains text too | + +## 5b. Documentation distractors (comment2context, N=80, official documents) + +Diagnostic: the same full rankings with documentation files (`.md`, `.rst`, `.txt`, `.adoc`, `.html`, or under `docs/`/`doc/`) removed, applied identically to RepoNeedle and BM25. No comment2context gold file is documentation, so removal cannot drop gold files; this is an **oracle-style upper bound**, not a deployable filter. Fidelity: metrics with documentation included equal the audit's full-ranking metrics on 80 / 80 queries. Results: `doc_distractor/`. + +| comment2context (N=80) | MRR | Recall@5 | Recall@10 | Recall@20 | Docs in top 10 | +| :--- | :---: | :---: | :---: | :---: | :---: | +| RepoNeedle | 0.1857 | 0.1646 | 0.2250 | 0.3146 | 2.24 | +| RepoNeedle, documentation removed | 0.2033 | 0.2021 | 0.2729 | 0.3604 | — | +| BM25 | 0.1974 | 0.2146 | 0.4042 | 0.5292 | 0.71 | +| BM25, documentation removed | 0.2138 | 0.2521 | 0.4208 | 0.5458 | — | + +| Comparison (repo-clustered 95% CI) | Δ MRR | Δ Recall@20 | +| :--- | :---: | :---: | +| RepoNeedle without docs − with docs | **+0.0176 [+0.0105, +0.0232]** (53 wins / 0 losses) | **+0.0458 [+0.0098, +0.0905]** | +| BM25 without docs − with docs | +0.0164 [+0.0019, +0.0482] | +0.0167 [+0.0000, +0.0485] | +| RepoNeedle without docs − BM25 without docs | −0.0105 [−0.1018, +0.0656] | −0.1854 [−0.3556, −0.0578] | + +RepoNeedle ranks a median of 4 (mean 49.8) documentation files above its best-ranked gold file. Documentation distractors are a real, significant contributor for RepoNeedle, but they explain roughly a fifth of the Recall@20 gap; document representation explains most of it. + +## 6. Problem classification + +| Problem class | Present? | Size of effect (comment2context unless noted) | Evidence | +| :--- | :--- | :--- | :--- | +| **Evaluator methodology** (unpacked files ranked in corpus order) | **Yes, all tasks** | Recall@20 under-measured by +0.196 overall, +0.121 comment2context; MRR by +0.023 overall | §2–§3; 96 lost top-20 hits overall | +| **Document representation** (harness feeds one fragment per file) | **Yes, largest** | Recall@20 +0.175 for RepoNeedle; BM25 degrades identically when given the same fragments | §5a | +| **Documentation distractors** | **Yes, secondary** | MRR +0.018, Recall@20 +0.046 (oracle upper bound) | §5b | +| **Retrieval ranking** (residual) | **Yes, smaller** | RepoNeedle lexical-only vs ARB whole-file BM25 on equal input: Recall@20 0.4646 vs 0.5792. Fused vs BM25 on whole files: not significant | §5a | +| **Context packing** (CUD-Pack) | **No** | Packing the same ranking with ARB's packer gives identical BCY@8k; packed coverage rises with better input | §2, §5a | +| Graph dilution | No | Structural score is 0 for every file | §4 | +| Semantic noise | Not supported | Semantic-only outperforms lexical-only on official documents | §5 | +| Task routing | Not in the benchmark (labels are explicit); **yes for AUTO users** | AUTO would assign the labelled task to only 125 / 345 queries (36%) | §5 | +| Conversational query handling | **Not testable on ARB v2** | 0 of 345 queries are conversational; all are structured JSON | §5 | + +## 7. Decision + +Gate outcome against the Phase 0 criteria: +- **The evaluator assigns misleading ranks to unselected files: confirmed.** The corrected diagnostic evaluator is provided as `benchmarks/run_arb_evaluator_audit.py` (experimental, separate output directory). The official harness and published metric definitions are unchanged. +- **Full-ranking deficit for comment2context: not confirmed under realistic input.** It is significant only when RepoNeedle receives file fragments; with whole files it is not significant (MRR +0.006, Recall@20 −0.040, CIs span zero). +- **Packed-context coverage is limited by the ranking and its inputs, not by CUD-Pack.** + +Therefore Adaptive Query Routing 2.0's **conversational fusion re-weighting is not supported by the evidence** and should not be implemented now. + +## 8. Engineering recommendation + +**Address first: benchmark input fidelity and full-ranking evaluation** (measurement, no product change). Promote the diagnostic into a clearly labelled "corrected" evaluation that (1) gives RepoNeedle whole-file documents reconstructed from ARB chunks, the same text BM25 ranks, and (2) reports full-ranking MRR/Recall@K alongside packed-context coverage, next to the unchanged official numbers. *Evidence*: this is the single largest effect (+0.175 Recall@20) and it changes the sign of the comment2context conclusion. Any ranking change tuned on the current harness would optimise a fragment artifact. + +**Then, the product fix with the best evidence: documentation-distractor handling.** RepoNeedle already has an `enable_doc_filter` penalty, off by default. The oracle bound is +0.018 MRR on comment2context (CI excludes zero, 0 losses). It must be validated on repository-held-out splits under the corrected evaluator across **all four tasks** (1% of edit2ripple gold files are documentation) and against documentation-seeking queries before any default change. + +**Then: the residual lexical gap** (RepoNeedle field-weighted BM25 vs ARB BM25 on identical whole-file input: Recall@20 0.4646 vs 0.5792). Likely candidates to investigate: length normalisation, field weights, and JSON key tokens in structured queries. + +**Separately: AUTO intent classification** (36% agreement with ARB task labels). It does not affect the published task-conditioned numbers, but it does affect real users. ARB's task labels allow a classifier accuracy evaluation with repository-level splits. + +**Postpone:** +- Conversational fusion weights (lexical 0.80 / semantic 0.15 / structural 0.05). Lexical-only underperforms the current fusion on both document representations, and ARB v2 has no conversational queries to validate against. +- Semantic noise filtering (not supported by the signal analysis). +- Graph / PageRank changes (zero structural contribution in these configurations). +- CUD-Pack changes (not a bottleneck). +- README or headline claim updates (diagnostic results only; not official benchmark results). + +**Expectation-setting**: the evaluator and representation corrections change how much of RepoNeedle's ranking quality is *measured*; they do not change the product. Product-level MRR gains should be claimed only from validated ranking changes, measured under the corrected evaluator on held-out repositories. + + diff --git a/reports/experimental/phase0_evaluator_audit/SUMMARY.json b/reports/experimental/phase0_evaluator_audit/SUMMARY.json new file mode 100644 index 0000000..1294395 --- /dev/null +++ b/reports/experimental/phase0_evaluator_audit/SUMMARY.json @@ -0,0 +1,563 @@ +{ + "n_samples": 345, + "fidelity": { + "official_sequence_reproduces_current_engine_run": "345/345", + "bm25_reproduces_historical_official_bm25": "345/345", + "bm25_bcy8k_reproduces_historical": "345/345" + }, + "overall": { + "n": 345, + "official": { + "mrr": 0.21532780783702646, + "recall@5": 0.3032367149758454, + "recall@10": 0.36772946859903377, + "recall@20": 0.38415458937198066 + }, + "rn_full": { + "mrr": 0.23793594320609768, + "recall@5": 0.3061352657004831, + "recall@10": 0.4406763285024154, + "recall@20": 0.5802898550724638 + }, + "rn_lexical_only": { + "mrr": 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"gold": false}, {"rank": 15, "path": "tests/tox_env/python/test_python_runner.py", "score": 61.3875, "gold": false}, {"rank": 16, "path": "docs/changelog.rst", "score": 59.2957, "gold": false}, {"rank": 17, "path": "tests/config/source/test_toml_pyproject.py", "score": 58.9863, "gold": false}, {"rank": 18, "path": "tox.toml", "score": 56.3056, "gold": false}, {"rank": 19, "path": ".github/ISSUE_TEMPLATE/feature-request.md", "score": 55.3705, "gold": false}, {"rank": 20, "path": "tests/config/source/test_toml_tox.py", "score": 54.0234, "gold": false}, {"rank": 21, "path": "src/tox/tox_env/python/pip/req_file.py", "score": 53.4167, "gold": false}, {"rank": 22, "path": "src/tox/util/graph.py", "score": 52.2087, "gold": false}, {"rank": 23, "path": "tests/tox_env/python/test_python_api.py", "score": 51.9344, "gold": false}, {"rank": 24, "path": "tests/session/cmd/test_list_envs.py", "score": 51.8581, "gold": false}, {"rank": 25, "path": "tests/config/test_set_env.py", "score": 51.1292, "gold": false}]}} diff --git a/reports/experimental/phase1_product_aligned_evaluation/REPORT.md b/reports/experimental/phase1_product_aligned_evaluation/REPORT.md new file mode 100644 index 0000000..61207d5 --- /dev/null +++ b/reports/experimental/phase1_product_aligned_evaluation/REPORT.md @@ -0,0 +1,122 @@ +# Supplemental Product-Aligned Evaluation (ARB v2 positives) + +> [!IMPORTANT] +> **This is a supplemental, product-aligned evaluation. It is NOT an official ARB v2 score and does not replace one.** The official ARB v2 results (README Part A, measured on 2026-10-28 with the engine at commit `8e00425`), the official current-engine re-measurement (`reports/arb_v2_current_engine/`), the official harnesses, and the ARB metric definitions are all unchanged. + +**Date**: 2026-11-09 · **Engine**: development branch (identical to released 1.1.0 for ranking) · **Evaluator**: `benchmarks/run_arb_product_aligned_eval.py` · **Configuration**: `config.json` (fixed before any result was computed) · **Run metadata**: `run_metadata.json` + +--- + +## 1. Why this evaluation exists + +The Phase 0 audit (`../phase0_evaluator_audit/PHASE0_REPORT.md`) found two properties of the official harness that make it measure something other than RepoNeedle's real retrieval behaviour: +1. **Input representation**: RepoNeedle receives one fragment per file (the single longest ARB chunk), while the official BM25 baseline ranks every chunk of every file. In real use, RepoNeedle reads whole files. +2. **Ranking measurement**: files RepoNeedle does not pack into the 8,000-token context are appended in corpus order and scored as if ranked. + +This evaluation measures RepoNeedle's **full ranking** on **both** representations and compares it with BM25 given the **same** representation. + +## 2. Evaluation architecture and protocol + +| Item | Decision | +| :--- | :--- | +| Queries / gold | All 345 ARB v2 positive queries (`code2test` 106, `comment2context` 80, `trace2code` 101, `edit2ripple` 58); gold = ARB `target_gold_files` | +| Retrieval unit | **File**, for every system | +| Candidate universe | Every path with at least one chunk in the snapshot's ARB chunk file (same universe for all systems) | +| Fragment representation | Longest ARB chunk per path (what the official harness gives RepoNeedle) | +| Whole-file representation | The `file` chunk's header lines before the first symbol, then every `symbol` chunk in line order | +| RepoNeedle ranking | The complete ranking the engine hands to CUD-Pack (fused score, all files); task label passed as in the official harness | +| BM25 | ARB's own BM25 (k1 = 1.5, b = 0.75, ARB tokenizer). File-unit variants index one document per file. The chunk variant (the official baseline) maps chunk scores to files by **max**, which is ARB's own `unique_ranked_paths` rule | +| Ties | Score descending, then path ascending (asserted against both engines' native order) | +| Full-ranking metrics | MRR, Recall@5/10/20, per-query reciprocal rank, gold positions (ARB `reciprocal_rank` / `recall_at`, cross-checked by an independent implementation) | +| Packed context | CUD-Pack output at 8,000 tokens: gold coverage, queries with at least one gold file packed, utilisation, files packed | +| Remainder | Count of unselected files only. **No positions are assigned**; remainder order never enters a full-ranking metric | +| Official sequence | Packed files then corpus order, recorded **only** to check fidelity against the official current-engine run | +| Statistics | Paired per-query differences; repository-clustered percentile bootstrap (resample repositories with replacement), 10,000 resamples, seed 20261109, 95% CI. "Supported" means the CI excludes zero | + +**Valid comparisons.** "RepoNeedle vs BM25" is like-for-like only within a representation (fragment vs fragment, whole file vs whole file). The comparison with the official chunk-level BM25 is reported for continuity, but its BM25 uses a different retrieval unit (chunks aggregated by max) and different input text, so it is **not** a matched comparison. + +## 3. Fidelity and reproducibility + +| Check | Result | +| :--- | :--- | +| Queries present / expected | **345 / 345**; 0 missing, 0 unexpected, 0 duplicate rows | +| Missing gold labels | 0 | +| Task and repository of every query match the dataset | 345 / 345 | +| Every ranked file belongs to the query's snapshot; no duplicate files in any ranking | asserted for every ranking of every query | +| Independent metric implementation agrees with ARB helpers | 345 / 345 | +| Hoisted BM25 equals ARB `rank_chunks_bm25_with_scores` | asserted on the first query of every snapshot, for all three BM25 variants | +| Official sequence equals the official current-engine run (`reports/arb_v2_current_engine/`) | **345 / 345** | +| BM25 (chunks) equals the historical official BM25 (`reports/full_arb_validation/`) | **345 / 345** | +| Independent reproduction of the Phase 0 audit | RepoNeedle fragment 345 / 345; RepoNeedle whole file, BM25 whole file and BM25 fragment 238 / 238 (every query Phase 0 completed) | +| Determinism (fresh re-run of the first 6 snapshots: ranking hashes of all 5 systems + all metrics) | **8 / 8 queries identical** (`determinism_check.json`) | +| Historical result files unchanged | verified by SHA-256 before and after (see §8) | + +## 4. Results + +Complete tables, including every comparison, metric and task: `TABLES.md` (generated from `aggregate.json` and `confidence_intervals.json`). Per-query metrics, reciprocal ranks and gold positions: `per_query/`. Top-100 rankings: `rankings/`. + +### 4.1 Full-ranking metrics + +| Group (queries / repositories) | RN fragment MRR / R@20 | **RN whole-file MRR / R@20** | BM25 fragment MRR / R@20 | **BM25 whole-file MRR / R@20** | BM25 chunks (official) MRR / R@20 | +| :--- | :---: | :---: | :---: | :---: | :---: | +| **Overall** (345 / 25) | 0.2379 / 0.5803 | **0.2789 / 0.7042** | 0.1439 / 0.3604 | **0.1717 / 0.5138** | 0.1520 / 0.4452 | +| comment2context (80 / 18) | 0.1857 / 0.3146 | 0.2037 / 0.4896 | 0.1875 / 0.3563 | 0.2063 / 0.5792 | 0.1974 / 0.5292 | +| code2test (106 / 12) | 0.2736 / 0.6434 | 0.3556 / 0.7698 | 0.0656 / 0.2594 | 0.1160 / 0.3381 | 0.1057 / 0.3066 | +| trace2code (101 / 7) | 0.2577 / 0.6782 | 0.2885 / 0.8300 | 0.1432 / 0.3927 | 0.1476 / 0.6089 | 0.1638 / 0.4934 | +| edit2ripple (58 / 10) | 0.2104 / 0.6609 | 0.2259 / 0.6609 | 0.2283 / 0.4943 | 0.2676 / 0.5790 | 0.1536 / 0.4986 | + +### 4.2 Matched comparisons (paired, repository-clustered 95% CI) + +| Group | RN − BM25, **whole file**: Δ MRR | Δ Recall@20 | RN − BM25, fragment: Δ MRR | Δ Recall@20 | RN whole file − RN fragment: Δ MRR | Δ Recall@20 | +| :--- | :---: | :---: | :---: | :---: | :---: | :---: | +| **Overall** | **+0.1073 [+0.0647, +0.1596]** | **+0.1904 [+0.1114, +0.2715]** | **+0.0940 [+0.0570, +0.1201]** | **+0.2199 [+0.1278, +0.2938]** | **+0.0410 [+0.0007, +0.1042]** | **+0.1239 [+0.0572, +0.2430]** | +| comment2context | −0.0026 [−0.0297, +0.0337] | **−0.0896 [−0.1779, −0.0056]** | −0.0018 [−0.0329, +0.0399] | −0.0417 [−0.1240, +0.0515] | +0.0180 [−0.0362, +0.0772] | **+0.1750 [+0.0915, +0.2909]** | +| code2test | **+0.2397 [+0.1783, +0.2994]** | **+0.4318 [+0.3207, +0.5229]** | **+0.2080 [+0.1672, +0.2511]** | **+0.3840 [+0.2544, +0.4880]** | **+0.0821 [+0.0098, +0.1417]** | **+0.1264 [+0.0790, +0.1785]** | +| trace2code | +0.1409 [−0.0344, +0.2275] | +0.2211 [−0.0358, +0.4056] | +0.1145 [−0.0396, +0.1619] | **+0.2855 [+0.1000, +0.3373]** | +0.0307 [−0.0658, +0.2125] | +0.1518 [−0.0097, +0.4725] | +| edit2ripple | −0.0417 [−0.1095, +0.0466] | **+0.0819 [+0.0145, +0.1771]** | −0.0179 [−0.0472, +0.0245] | **+0.1667 [+0.0861, +0.2519]** | **+0.0155 [+0.0010, +0.0294]** | +0.0000 [−0.0660, +0.0510] | + +Bold = statistically supported (CI excludes zero). Unbolded differences are descriptive only. + +### 4.3 Packed context (CUD-Pack, 8,000 tokens) + +| Group | Gold coverage: fragment → whole file | Queries with ≥1 gold packed: fragment → whole file | Files packed (whole file) | Utilisation | +| :--- | :---: | :---: | :---: | :---: | +| **Overall** | 38.3% → **59.8%** | 45.8% → **68.4%** | 11.7 | 99.5% | +| comment2context | 19.4% → 35.8% | 32.5% → 51.2% | 11.0 | 99.3% | +| code2test | 51.8% → 68.3% | 58.5% → 74.5% | 12.3 | 99.4% | +| trace2code | 35.5% → 70.8% | 37.6% → 75.2% | 12.1 | 99.6% | +| edit2ripple | 44.5% → 58.3% | 55.2% → 69.0% | 10.7 | 99.5% | + +### 4.4 Latency (descriptive only) + +Per-query query-time latency, measured with 4 shards running concurrently (overall means: RepoNeedle `retrieve()` 549 ms on fragments vs 1,368 ms on whole files; p50 222 ms vs 499 ms). BM25 timings use the hoisted tokenisation described above and exclude it, so **RepoNeedle and BM25 latencies are not comparable** with each other or with README Part D. Whole-file documents make RepoNeedle's per-query work (including packing larger files) about 2–2.5× slower than on fragments. + +## 5. Findings + +1. **On matched input, RepoNeedle's full ranking is significantly better than BM25 overall**, on both representations (whole file: MRR +0.107, Recall@20 +0.190). +2. **Input representation matters for both systems.** Whole files improve RepoNeedle (Recall@20 +0.124 overall) and BM25 (+0.153) alike. The official harness therefore under-states both, but especially RepoNeedle's packed context (gold coverage 38% → 60%). +3. **comment2context remains a genuine weakness on deep recall.** Against BM25 given the same whole files, RepoNeedle is level on MRR but significantly worse on Recall@20 (−0.090). This refines Phase 0, whose "not significant" result compared against the chunk-level official BM25. It is consistent with Phase 0's residual lexical gap and documentation-distractor findings. +4. **code2test is RepoNeedle's clearest strength**, significant on every metric and representation. +5. **trace2code results are inconclusive at the cluster level.** Point estimates favour RepoNeedle, but the 101 queries come from only 7 repositories, and most matched CIs include zero. +6. **edit2ripple**: RepoNeedle is significantly better on Recall@10/20 with whole files. MRR differences are not significant. + +## 6. Limitations + +- **Small numbers of repository clusters**: 25 overall; 18, 12, 7 and 10 per task. Percentile bootstraps with few clusters (especially trace2code's 7) can under-cover, so treat per-task intervals as approximate. +- **Reconstructed whole files are approximations.** Symbol chunks are themselves capped at 8,000 characters, and header text beyond the `file` chunk's preview is lost. Reconstruction is close to, but not exactly, the source file. +- **Benchmark queries are structured JSON** (PR review context, PR descriptions, CI failures, diffs). Results do not speak to conversational natural-language queries. +- **Task labels are passed explicitly**, as in the official harness. AUTO intent classification is not evaluated here (Phase 0: 36% agreement with labels). +- **Single benchmark.** ARB v2 positives only; no held-out benchmark was used, and no parameter was tuned in this evaluation. +- Latency is descriptive only (§4.4). + +## 7. Recommendation + +The methodology is now reliable enough to proceed with **documentation-filter validation**, under these conditions: +- Use this evaluator's **whole-file full-ranking** view as the primary metric, alongside packed coverage, and keep the official harness numbers reported unchanged. +- Fix a **repository-level dev/test split before** looking at filter results (for example, by stratifying the 25 repositories across tasks). Choose the penalty on dev only, and report test once. +- Evaluate all four tasks, since a filter that helps comment2context could hurt tasks where documentation is occasionally gold (1% of edit2ripple gold). +- Pre-register the success criterion: a supported improvement on comment2context Recall@20 or MRR, and no supported regression on any other task. + +## 8. Reproduction + +See `REPRODUCE.md`. diff --git a/reports/experimental/phase1_product_aligned_evaluation/REPRODUCE.md b/reports/experimental/phase1_product_aligned_evaluation/REPRODUCE.md new file mode 100644 index 0000000..80c7809 --- /dev/null +++ b/reports/experimental/phase1_product_aligned_evaluation/REPRODUCE.md @@ -0,0 +1,29 @@ +# Reproducing the Supplemental Product-Aligned Evaluation + +Requirements: this repository, the Agent Retrieval Bench checkout at `external/agent-retrieval-bench` (with `data/corpus/v2_selective_mixed` and the `v2_*` benchmark splits), Python 3.10+, and the package dependencies. About 7 GB of free RAM is recommended for 4 shards. + +```bash +# 1. Evaluate (4 resumable shards; each skips queries already written) +python benchmarks/run_arb_product_aligned_eval.py --shard 0 --num-shards 4 +python benchmarks/run_arb_product_aligned_eval.py --shard 1 --num-shards 4 +python benchmarks/run_arb_product_aligned_eval.py --shard 2 --num-shards 4 +python benchmarks/run_arb_product_aligned_eval.py --shard 3 --num-shards 4 + +# 2. Merge: writes aggregate.json, confidence_intervals.json, fidelity.json, run_metadata.json +python benchmarks/run_arb_product_aligned_eval.py --merge + +# 3. Determinism: re-runs the first N snapshots in a temporary directory and compares +python benchmarks/run_arb_product_aligned_eval.py --check-determinism 6 + +# 4. Tables used by REPORT.md +python benchmarks/render_product_aligned_tables.py + +# 5. Unit tests for the evaluator's protocol helpers +python -m pytest tests/test_product_aligned_eval.py -q +``` + +Notes: +- The protocol (retrieval unit, representations, tie rule, chunk-to-file rule, bootstrap settings) is fixed in the script's `CONFIG` and written to `config.json` on first run. +- The merge step cross-checks against `reports/arb_v2_current_engine/` (official sequence), `reports/full_arb_validation/` (historical BM25), and `reports/experimental/phase0_evaluator_audit/` (Phase 0), all read-only. +- The bootstrap is deterministic (seed 20261109); re-running `--merge` reproduces the confidence intervals exactly. +- Run 2026-11-09 with 4 concurrent shards. Wall time was about 55 minutes on a 16 GB, 18-thread Windows machine. diff --git a/reports/experimental/phase1_product_aligned_evaluation/TABLES.md b/reports/experimental/phase1_product_aligned_evaluation/TABLES.md new file mode 100644 index 0000000..24554c4 --- /dev/null +++ b/reports/experimental/phase1_product_aligned_evaluation/TABLES.md @@ -0,0 +1,231 @@ +# Tables (generated from aggregate.json and confidence_intervals.json) + +## overall (queries = 345, repositories = 25) + +| System | MRR | Recall@5 | Recall@10 | Recall@20 | +| :--- | :---: | :---: | :---: | :---: | +| RepoNeedle, fragment input | 0.2379 | 0.3061 | 0.4407 | 0.5803 | +| RepoNeedle, whole-file input | 0.2789 | 0.4270 | 0.5862 | 0.7042 | +| BM25, fragment input (file unit) | 0.1439 | 0.1650 | 0.2389 | 0.3604 | +| BM25, whole-file input (file unit) | 0.1717 | 0.2290 | 0.3498 | 0.5138 | +| BM25, all chunks → file by max (official baseline) | 0.1520 | 0.1899 | 0.3034 | 0.4452 | + +| Packed context (CUD-Pack, 8,000 tokens) | Files packed | Utilisation | Gold coverage | Queries with ≥1 gold packed | Unselected files | +| :--- | :---: | :---: | :---: | :---: | :---: | +| fragment input | 8.9 | 99.6% | 38.3% | 45.8% | 1615 (no positions assigned) | +| wholefile input | 11.7 | 99.5% | 59.8% | 68.4% | 1612 (no positions assigned) | + +| Comparison (paired, repo-clustered 95% CI) | Metric | Mean Δ | W / T / L | 95% CI | CI excludes 0 | +| :--- | :--- | :---: | :---: | :---: | :---: | +| Representation effect on RepoNeedle | mrr | +0.0410 | 165 / 73 / 107 | [+0.0007, +0.1042] | **yes** | +| Representation effect on RepoNeedle | recall@5 | +0.1209 | 66 / 261 / 18 | [+0.0503, +0.2422] | **yes** | +| Representation effect on RepoNeedle | recall@10 | +0.1455 | 76 / 257 / 12 | [+0.0725, +0.2684] | **yes** | +| Representation effect on RepoNeedle | recall@20 | +0.1239 | 68 / 265 / 12 | [+0.0572, +0.2430] | **yes** | +| Representation effect on BM25 | mrr | +0.0277 | 191 / 37 / 117 | [+0.0080, +0.0500] | **yes** | +| Representation effect on BM25 | recall@5 | +0.0640 | 50 / 277 / 18 | [+0.0270, +0.0992] | **yes** | +| Representation effect on BM25 | recall@10 | +0.1109 | 64 / 266 / 15 | [+0.0624, +0.1616] | **yes** | +| Representation effect on BM25 | recall@20 | +0.1534 | 78 / 252 / 15 | [+0.1142, +0.2200] | **yes** | +| RepoNeedle vs BM25, matched fragment input | mrr | +0.0940 | 239 / 33 / 73 | [+0.0570, +0.1201] | **yes** | +| RepoNeedle vs BM25, matched fragment input | recall@5 | +0.1412 | 76 / 249 / 20 | [+0.0785, +0.1873] | **yes** | +| RepoNeedle vs BM25, matched fragment input | recall@10 | +0.2018 | 96 / 228 / 21 | [+0.1203, +0.2651] | **yes** | +| RepoNeedle vs BM25, matched fragment input | recall@20 | +0.2199 | 107 / 218 / 20 | [+0.1278, +0.2938] | **yes** | +| RepoNeedle vs BM25, matched whole-file input | mrr | +0.1073 | 233 / 28 / 84 | [+0.0647, +0.1596] | **yes** | +| RepoNeedle vs BM25, matched whole-file input | recall@5 | +0.1980 | 100 / 218 / 27 | [+0.0952, +0.3102] | **yes** | +| RepoNeedle vs BM25, matched whole-file input | recall@10 | +0.2364 | 116 / 196 / 33 | [+0.1152, +0.3551] | **yes** | +| RepoNeedle vs BM25, matched whole-file input | recall@20 | +0.1904 | 97 / 226 / 22 | [+0.1114, +0.2715] | **yes** | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | mrr | +0.1269 | 250 / 20 / 75 | [+0.0921, +0.1671] | **yes** | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | recall@5 | +0.2371 | 114 / 210 / 21 | [+0.1325, +0.3180] | **yes** | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | recall@10 | +0.2828 | 135 / 180 / 30 | [+0.1650, +0.3681] | **yes** | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | recall@20 | +0.2590 | 127 / 193 / 25 | [+0.1737, +0.3242] | **yes** | + +| Latency per query (ms, 4 concurrent shards) | Mean | p50 | +| :--- | :---: | :---: | +| rn_fragment_retrieve | 549.2 | 222.1 | +| rn_wholefile_retrieve | 1368.4 | 498.5 | +| bm25_chunks | 1140.9 | 178.6 | +| bm25_fragment | 74.9 | 18.5 | +| bm25_wholefile | 93.1 | 16.6 | + +## comment2context (queries = 80, repositories = 18) + +| System | MRR | Recall@5 | Recall@10 | Recall@20 | +| :--- | :---: | :---: | :---: | :---: | +| RepoNeedle, fragment input | 0.1857 | 0.1646 | 0.2250 | 0.3146 | +| RepoNeedle, whole-file input | 0.2037 | 0.2458 | 0.3521 | 0.4896 | +| BM25, fragment input (file unit) | 0.1875 | 0.1875 | 0.2542 | 0.3563 | +| BM25, whole-file input (file unit) | 0.2063 | 0.2812 | 0.4333 | 0.5792 | +| BM25, all chunks → file by max (official baseline) | 0.1974 | 0.2146 | 0.4042 | 0.5292 | + +| Packed context (CUD-Pack, 8,000 tokens) | Files packed | Utilisation | Gold coverage | Queries with ≥1 gold packed | Unselected files | +| :--- | :---: | :---: | :---: | :---: | :---: | +| fragment input | 10.1 | 99.5% | 19.4% | 32.5% | 2303 (no positions assigned) | +| wholefile input | 11.0 | 99.3% | 35.8% | 51.2% | 2302 (no positions assigned) | + +| Comparison (paired, repo-clustered 95% CI) | Metric | Mean Δ | W / T / L | 95% CI | CI excludes 0 | +| :--- | :--- | :---: | :---: | :---: | :---: | +| Representation effect on RepoNeedle | mrr | +0.0180 | 43 / 9 / 28 | [-0.0362, +0.0772] | no | +| Representation effect on RepoNeedle | recall@5 | +0.0813 | 14 / 61 / 5 | [+0.0199, +0.1562] | **yes** | +| Representation effect on RepoNeedle | recall@10 | +0.1271 | 19 / 58 / 3 | [+0.0625, +0.2180] | **yes** | +| Representation effect on RepoNeedle | recall@20 | +0.1750 | 24 / 53 / 3 | [+0.0915, +0.2909] | **yes** | +| Representation effect on BM25 | mrr | +0.0188 | 43 / 9 / 28 | [-0.0289, +0.0749] | no | +| Representation effect on BM25 | recall@5 | +0.0938 | 18 / 55 / 7 | [-0.0014, +0.1902] | no | +| Representation effect on BM25 | recall@10 | +0.1792 | 24 / 52 / 4 | [+0.0682, +0.2868] | **yes** | +| Representation effect on BM25 | recall@20 | +0.2229 | 26 / 51 / 3 | [+0.1039, +0.3682] | **yes** | +| RepoNeedle vs BM25, matched fragment input | mrr | -0.0018 | 35 / 13 / 32 | [-0.0329, +0.0399] | no | +| RepoNeedle vs BM25, matched fragment input | recall@5 | -0.0229 | 4 / 69 / 7 | [-0.0601, +0.0084] | no | +| RepoNeedle vs BM25, matched fragment input | recall@10 | -0.0292 | 3 / 68 / 9 | [-0.0756, +0.0282] | no | +| RepoNeedle vs BM25, matched fragment input | recall@20 | -0.0417 | 7 / 61 / 12 | [-0.1240, +0.0515] | no | +| RepoNeedle vs BM25, matched whole-file input | mrr | -0.0026 | 32 / 10 / 38 | [-0.0297, +0.0337] | no | +| RepoNeedle vs BM25, matched whole-file input | recall@5 | -0.0354 | 7 / 59 / 14 | [-0.1205, +0.0647] | no | +| RepoNeedle vs BM25, matched whole-file input | recall@10 | -0.0813 | 7 / 54 / 19 | [-0.1594, +0.0287] | no | +| RepoNeedle vs BM25, matched whole-file input | recall@20 | -0.0896 | 6 / 60 / 14 | [-0.1779, -0.0056] | **yes** | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | mrr | +0.0063 | 38 / 12 / 30 | [-0.0481, +0.0576] | no | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | recall@5 | +0.0312 | 11 / 61 / 8 | [-0.0316, +0.0786] | no | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | recall@10 | -0.0521 | 13 / 49 / 18 | [-0.1952, +0.0675] | no | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | recall@20 | -0.0396 | 14 / 49 / 17 | [-0.1961, +0.1120] | no | + +| Latency per query (ms, 4 concurrent shards) | Mean | p50 | +| :--- | :---: | :---: | +| rn_fragment_retrieve | 411.0 | 232.5 | +| rn_wholefile_retrieve | 1049.3 | 635.5 | +| bm25_chunks | 1587.8 | 361.4 | +| bm25_fragment | 88.4 | 33.1 | +| bm25_wholefile | 85.2 | 33.5 | + +## code2test (queries = 106, repositories = 12) + +| System | MRR | Recall@5 | Recall@10 | Recall@20 | +| :--- | :---: | :---: | :---: | :---: | +| RepoNeedle, fragment input | 0.2736 | 0.3918 | 0.5742 | 0.6434 | +| RepoNeedle, whole-file input | 0.3556 | 0.4818 | 0.6579 | 0.7698 | +| BM25, fragment input (file unit) | 0.0656 | 0.0770 | 0.1462 | 0.2594 | +| BM25, whole-file input (file unit) | 0.1160 | 0.1336 | 0.2453 | 0.3381 | +| BM25, all chunks → file by max (official baseline) | 0.1057 | 0.1305 | 0.2186 | 0.3066 | + +| Packed context (CUD-Pack, 8,000 tokens) | Files packed | Utilisation | Gold coverage | Queries with ≥1 gold packed | Unselected files | +| :--- | :---: | :---: | :---: | :---: | :---: | +| fragment input | 9.0 | 99.6% | 51.8% | 58.5% | 2260 (no positions assigned) | +| wholefile input | 12.3 | 99.4% | 68.3% | 74.5% | 2257 (no positions assigned) | + +| Comparison (paired, repo-clustered 95% CI) | Metric | Mean Δ | W / T / L | 95% CI | CI excludes 0 | +| :--- | :--- | :---: | :---: | :---: | :---: | +| Representation effect on RepoNeedle | mrr | +0.0821 | 52 / 23 / 31 | [+0.0098, +0.1417] | **yes** | +| Representation effect on RepoNeedle | recall@5 | +0.0899 | 19 / 80 / 7 | [+0.0092, +0.1560] | **yes** | +| Representation effect on RepoNeedle | recall@10 | +0.0836 | 18 / 82 / 6 | [+0.0299, +0.1356] | **yes** | +| Representation effect on RepoNeedle | recall@20 | +0.1264 | 22 / 82 / 2 | [+0.0790, +0.1785] | **yes** | +| Representation effect on BM25 | mrr | +0.0504 | 63 / 3 / 40 | [+0.0150, +0.0895] | **yes** | +| Representation effect on BM25 | recall@5 | +0.0566 | 13 / 89 / 4 | [-0.0063, +0.1292] | no | +| Representation effect on BM25 | recall@10 | +0.0991 | 16 / 87 / 3 | [+0.0129, +0.2143] | **yes** | +| Representation effect on BM25 | recall@20 | +0.0786 | 15 / 86 / 5 | [+0.0352, +0.1317] | **yes** | +| RepoNeedle vs BM25, matched fragment input | mrr | +0.2080 | 97 / 2 / 7 | [+0.1672, +0.2511] | **yes** | +| RepoNeedle vs BM25, matched fragment input | recall@5 | +0.3148 | 45 / 56 / 5 | [+0.2033, +0.4207] | **yes** | +| RepoNeedle vs BM25, matched fragment input | recall@10 | +0.4280 | 55 / 50 / 1 | [+0.2936, +0.5545] | **yes** | +| RepoNeedle vs BM25, matched fragment input | recall@20 | +0.3840 | 49 / 56 / 1 | [+0.2544, +0.4880] | **yes** | +| RepoNeedle vs BM25, matched whole-file input | mrr | +0.2397 | 92 / 5 / 9 | [+0.1783, +0.2994] | **yes** | +| RepoNeedle vs BM25, matched whole-file input | recall@5 | +0.3481 | 45 / 60 / 1 | [+0.2401, +0.4401] | **yes** | +| RepoNeedle vs BM25, matched whole-file input | recall@10 | +0.4126 | 52 / 51 / 3 | [+0.2880, +0.5133] | **yes** | +| RepoNeedle vs BM25, matched whole-file input | recall@20 | +0.4318 | 54 / 52 / 0 | [+0.3207, +0.5229] | **yes** | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | mrr | +0.2500 | 92 / 4 / 10 | [+0.1694, +0.3149] | **yes** | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | recall@5 | +0.3513 | 47 / 56 / 3 | [+0.2176, +0.4684] | **yes** | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | recall@10 | +0.4393 | 53 / 51 / 2 | [+0.3015, +0.5616] | **yes** | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | recall@20 | +0.4632 | 59 / 46 / 1 | [+0.3373, +0.5726] | **yes** | + +| Latency per query (ms, 4 concurrent shards) | Mean | p50 | +| :--- | :---: | :---: | +| rn_fragment_retrieve | 867.6 | 201.0 | +| rn_wholefile_retrieve | 2410.4 | 330.4 | +| bm25_chunks | 1464.5 | 278.0 | +| bm25_fragment | 78.7 | 27.2 | +| bm25_wholefile | 75.2 | 24.6 | + +## trace2code (queries = 101, repositories = 7) + +| System | MRR | Recall@5 | Recall@10 | Recall@20 | +| :--- | :---: | :---: | :---: | :---: | +| RepoNeedle, fragment input | 0.2577 | 0.2855 | 0.4538 | 0.6782 | +| RepoNeedle, whole-file input | 0.2885 | 0.5050 | 0.7046 | 0.8300 | +| BM25, fragment input (file unit) | 0.1432 | 0.1535 | 0.2277 | 0.3927 | +| BM25, whole-file input (file unit) | 0.1476 | 0.2079 | 0.3119 | 0.6089 | +| BM25, all chunks → file by max (official baseline) | 0.1638 | 0.2228 | 0.3218 | 0.4934 | + +| Packed context (CUD-Pack, 8,000 tokens) | Files packed | Utilisation | Gold coverage | Queries with ≥1 gold packed | Unselected files | +| :--- | :---: | :---: | :---: | :---: | :---: | +| fragment input | 8.1 | 99.7% | 35.5% | 37.6% | 248 (no positions assigned) | +| wholefile input | 12.1 | 99.6% | 70.8% | 75.2% | 244 (no positions assigned) | + +| Comparison (paired, repo-clustered 95% CI) | Metric | Mean Δ | W / T / L | 95% CI | CI excludes 0 | +| :--- | :--- | :---: | :---: | :---: | :---: | +| Representation effect on RepoNeedle | mrr | +0.0307 | 50 / 16 / 35 | [-0.0658, +0.2125] | no | +| Representation effect on RepoNeedle | recall@5 | +0.2195 | 28 / 68 / 5 | [+0.0455, +0.5880] | **yes** | +| Representation effect on RepoNeedle | recall@10 | +0.2508 | 31 / 67 / 3 | [+0.0000, +0.5914] | no | +| Representation effect on RepoNeedle | recall@20 | +0.1518 | 20 / 76 / 5 | [-0.0097, +0.4725] | no | +| Representation effect on BM25 | mrr | +0.0043 | 58 / 11 / 32 | [-0.0283, +0.0858] | no | +| Representation effect on BM25 | recall@5 | +0.0545 | 10 / 88 / 3 | [+0.0311, +0.1875] | **yes** | +| Representation effect on BM25 | recall@10 | +0.0842 | 14 / 83 / 4 | [+0.0517, +0.2500] | **yes** | +| Representation effect on BM25 | recall@20 | +0.2162 | 27 / 70 / 4 | [+0.1292, +0.3636] | **yes** | +| RepoNeedle vs BM25, matched fragment input | mrr | +0.1145 | 79 / 9 / 13 | [-0.0396, +0.1619] | no | +| RepoNeedle vs BM25, matched fragment input | recall@5 | +0.1320 | 16 / 82 / 3 | [-0.0106, +0.1782] | no | +| RepoNeedle vs BM25, matched fragment input | recall@10 | +0.2261 | 28 / 69 / 4 | [+0.1351, +0.2693] | **yes** | +| RepoNeedle vs BM25, matched fragment input | recall@20 | +0.2855 | 34 / 64 / 3 | [+0.1000, +0.3373] | **yes** | +| RepoNeedle vs BM25, matched whole-file input | mrr | +0.1409 | 80 / 8 / 13 | [-0.0344, +0.2275] | no | +| RepoNeedle vs BM25, matched whole-file input | recall@5 | +0.2970 | 37 / 56 / 8 | [-0.1053, +0.5914] | no | +| RepoNeedle vs BM25, matched whole-file input | recall@10 | +0.3927 | 45 / 53 / 3 | [-0.0476, +0.6505] | no | +| RepoNeedle vs BM25, matched whole-file input | recall@20 | +0.2211 | 27 / 71 / 3 | [-0.0358, +0.4056] | no | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | mrr | +0.1246 | 76 / 2 / 23 | [+0.0375, +0.2079] | **yes** | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | recall@5 | +0.2822 | 37 / 56 / 8 | [-0.1190, +0.4615] | no | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | recall@10 | +0.3828 | 45 / 51 / 5 | [+0.0000, +0.5222] | no | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | recall@20 | +0.3366 | 38 / 60 / 3 | [+0.1143, +0.4560] | **yes** | + +| Latency per query (ms, 4 concurrent shards) | Mean | p50 | +| :--- | :---: | :---: | +| rn_fragment_retrieve | 242.3 | 177.6 | +| rn_wholefile_retrieve | 553.7 | 412.3 | +| bm25_chunks | 84.6 | 43.2 | +| bm25_fragment | 7.0 | 3.3 | +| bm25_wholefile | 7.1 | 4.3 | + +## edit2ripple (queries = 58, repositories = 10) + +| System | MRR | Recall@5 | Recall@10 | Recall@20 | +| :--- | :---: | :---: | :---: | :---: | +| RepoNeedle, fragment input | 0.2104 | 0.3807 | 0.4713 | 0.6609 | +| RepoNeedle, whole-file input | 0.2259 | 0.4411 | 0.5718 | 0.6609 | +| BM25, fragment input (file unit) | 0.2283 | 0.3147 | 0.4066 | 0.4943 | +| BM25, whole-file input (file unit) | 0.2676 | 0.3678 | 0.4914 | 0.5790 | +| BM25, all chunks → file by max (official baseline) | 0.1536 | 0.2069 | 0.2874 | 0.4986 | + +| Packed context (CUD-Pack, 8,000 tokens) | Files packed | Utilisation | Gold coverage | Queries with ≥1 gold packed | Unselected files | +| :--- | :---: | :---: | :---: | :---: | :---: | +| fragment input | 8.3 | 99.5% | 44.5% | 55.2% | 1869 (no positions assigned) | +| wholefile input | 10.7 | 99.5% | 58.3% | 69.0% | 1866 (no positions assigned) | + +| Comparison (paired, repo-clustered 95% CI) | Metric | Mean Δ | W / T / L | 95% CI | CI excludes 0 | +| :--- | :--- | :---: | :---: | :---: | :---: | +| Representation effect on RepoNeedle | mrr | +0.0155 | 20 / 25 / 13 | [+0.0010, +0.0294] | **yes** | +| Representation effect on RepoNeedle | recall@5 | +0.0603 | 5 / 52 / 1 | [+0.0072, +0.1014] | **yes** | +| Representation effect on RepoNeedle | recall@10 | +0.1006 | 8 / 50 / 0 | [+0.0370, +0.1852] | **yes** | +| Representation effect on RepoNeedle | recall@20 | +0.0000 | 2 / 54 / 2 | [-0.0660, +0.0510] | no | +| Representation effect on BM25 | mrr | +0.0393 | 27 / 14 / 17 | [-0.0131, +0.0852] | no | +| Representation effect on BM25 | recall@5 | +0.0532 | 9 / 45 / 4 | [-0.0519, +0.1317] | no | +| Representation effect on BM25 | recall@10 | +0.0848 | 10 / 44 / 4 | [-0.0224, +0.1640] | no | +| Representation effect on BM25 | recall@20 | +0.0848 | 10 / 45 / 3 | [+0.0072, +0.1389] | **yes** | +| RepoNeedle vs BM25, matched fragment input | mrr | -0.0179 | 28 / 9 / 21 | [-0.0472, +0.0245] | no | +| RepoNeedle vs BM25, matched fragment input | recall@5 | +0.0661 | 11 / 42 / 5 | [-0.0023, +0.1471] | no | +| RepoNeedle vs BM25, matched fragment input | recall@10 | +0.0647 | 10 / 41 / 7 | [-0.0076, +0.1358] | no | +| RepoNeedle vs BM25, matched fragment input | recall@20 | +0.1667 | 17 / 37 / 4 | [+0.0861, +0.2519] | **yes** | +| RepoNeedle vs BM25, matched whole-file input | mrr | -0.0417 | 29 / 5 / 24 | [-0.1095, +0.0466] | no | +| RepoNeedle vs BM25, matched whole-file input | recall@5 | +0.0733 | 11 / 43 / 4 | [-0.0174, +0.2035] | no | +| RepoNeedle vs BM25, matched whole-file input | recall@10 | +0.0805 | 12 / 38 / 8 | [+0.0108, +0.1827] | **yes** | +| RepoNeedle vs BM25, matched whole-file input | recall@20 | +0.0819 | 10 / 43 / 5 | [+0.0145, +0.1771] | **yes** | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | mrr | +0.0723 | 44 / 2 / 12 | [+0.0317, +0.1311] | **yes** | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | recall@5 | +0.2342 | 19 / 37 / 2 | [+0.1037, +0.3316] | **yes** | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | recall@10 | +0.2845 | 24 / 29 / 5 | [+0.1758, +0.4232] | **yes** | +| RepoNeedle whole-file vs official chunk BM25 (different BM25 unit) | recall@20 | +0.1624 | 16 / 38 / 4 | [+0.0570, +0.2926] | **yes** | + +| Latency per query (ms, 4 concurrent shards) | Mean | p50 | +| :--- | :---: | :---: | +| rn_fragment_retrieve | 692.4 | 355.5 | +| rn_wholefile_retrieve | 1323.2 | 753.8 | +| bm25_chunks | 1772.6 | 352.5 | +| bm25_fragment | 168.0 | 27.4 | +| bm25_wholefile | 286.2 | 27.1 | diff --git a/reports/experimental/phase1_product_aligned_evaluation/aggregate.json b/reports/experimental/phase1_product_aligned_evaluation/aggregate.json new file mode 100644 index 0000000..be43992 --- /dev/null +++ b/reports/experimental/phase1_product_aligned_evaluation/aggregate.json @@ -0,0 +1,352 @@ +{ + "overall": { + "n_queries": 345, + "n_repositories": 25, + "rn_fragment": { + "mrr": 0.23793594320609768, + "recall@5": 0.3061352657004831, + "recall@10": 0.4406763285024154, + "recall@20": 0.5802898550724638 + }, + "rn_wholefile": { + "mrr": 0.27893748258911133, + "recall@5": 0.4270048309178744, + "recall@10": 0.586183574879227, + 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0.281147, 0.281105, 0.280814, 0.280808]}} diff --git a/reports/experimental/phase1_product_aligned_evaluation/run_metadata.json b/reports/experimental/phase1_product_aligned_evaluation/run_metadata.json new file mode 100644 index 0000000..27a59f6 --- /dev/null +++ b/reports/experimental/phase1_product_aligned_evaluation/run_metadata.json @@ -0,0 +1,9 @@ +{ + "git_head": "82d7fb3aa3c0868564e1f4b16c228359db8678d1", + "working_tree_dirty": true, + "python": "3.12.10", + "numpy": "2.5.2", + "platform": "Windows-11-10.0.26200-SP0", + "merged_at": "2026-11-09T19:01:45", + "reponeedle_version": "1.1.0" +} \ No newline at end of file diff --git a/reports/experimental/phase2_documentation_filter/REPORT.md b/reports/experimental/phase2_documentation_filter/REPORT.md new file mode 100644 index 0000000..699ca6c --- /dev/null +++ b/reports/experimental/phase2_documentation_filter/REPORT.md @@ -0,0 +1,157 @@ +# Milestone 2B: Documentation-Filter Validation on Held-Out Repositories + +> [!IMPORTANT] +> **EXPERIMENTAL. Not an official ARB score, and not a default change.** This evaluates the **existing**, opt-in documentation filter (`AblationConfig.enable_doc_filter`, `doc_filter_penalty`) with a pre-registered, repository-level development/test design. The filter stays **disabled by default**. Official ARB v2 results, the Phase 0 audit and the Phase 1 evaluation are unchanged. + +**Experiment script**: `benchmarks/run_doc_filter_validation.py` · **Framework**: Phase 1 product-aligned evaluation (file unit, whole-file input, full ranking; packed context reported separately) · **Engine**: development branch (ranking identical to released 1.1.0) · **Production code changed**: none + +--- + +## 1. The filter under test (established from the code before any experiment) + +| Question | Answer | +| :--- | :--- | +| What counts as documentation? | Path only (case-insensitive): contains `/doc/`, `/docs/`, `doc/`, `docs/`, `documentation/`, `guide/`, `tutorial/` or `.github/`, or ends in `.md`, `.rst`, `.txt`, `.adoc`, `.html`. No content or repository metadata is used. | +| Removal or penalty? | **Multiplicative penalty**: the fused score of each documentation file is multiplied by `doc_filter_penalty` (default 0.5). Files are never removed. | +| Stage | After score fusion (and optional graph diffusion), before sorting. It changes the **full ranking** and therefore the input to CUD-Pack. It does not affect candidate generation. | +| Scope | The default `legacy` ranker, identically for every task type. (`arm101` and `cascade` have separate, independent penalties not exercised here.) | +| Can documentation still be returned? | Yes. It stays in the ranking and can still be packed. | +| Can gold documentation be harmed? | Yes, by demotion (never removal). Measured in §6. | +| Configurable? | Yes: `doc_filter_penalty` float. No range validation exists; values in [0, 1] are meaningful (1 = no-op, 0 = documentation scored 0). | +| Known limitation | The predicate is broader than documentation: it also matches e.g. `requirements.txt`, `CMakeLists.txt`, `.github/` workflow files and any path containing `doc/` (e.g. `pydoc/`). Evaluated as-is, without modification. | + +Treatment arms pass `AblationConfig(enable_doc_filter=True, doc_filter_penalty=p)` and the control passes `AblationConfig()` per call to `retrieve()`, so the two arms share the same engine instance, input, candidate universe and code path. Packing mode is identical in both (`complete` at 8,000 tokens). Fidelity: the control arm reproduces the Phase 1 whole-file results exactly (full-ranking metrics and packed coverage, 166 / 166 development queries). + +## 2. Pre-registration (frozen before any filter result was computed) + +| Artifact | Value | +| :--- | :--- | +| `protocol.json` | Primary metric, guardrails, correction, grid, selection rule and decision rule (see below) | +| `split.json` | Repository → dev/test assignment, derived from repository ids and task coverage only | +| Split SHA-256 | `6b8b0322ef70d5ffbd072c8a10d7b0154bc166dc3650df85c1cb30a154ba5bb4` (canonical JSON) | +| Split seed / algorithm | `reponeedle-2B-split-v1`; shuffle with `random.Random(f"{seed}:{attempt}")`, test = first 10 of 25, accept the first attempt where every task's test repository count is within ±1 of round(0.4 n) and both splits cover every task. Accepted at attempt 0. | +| `selected_filter_config.json` SHA-256 | `30343e918194787a100251a3407d4d6d2dd69f51c4cbe9d7be7b8d987e9fa814` (frozen after dev, before test) | + +**Primary success criterion**: comment2context Recall@20, filter ON − OFF on the **test** repositories; the paired, repository-clustered 95% percentile CI must lie entirely above zero. + +**Guardrails** (7 hypotheses, one-sided regression tests, bootstrap p = (1 + #{resampled mean ≥ 0}) / (B + 1), Holm-Bonferroni at family-wise α = 0.05): comment2context MRR; MRR and Recall@20 for code2test, trace2code and edit2ripple. Recall@5 and Recall@10 are reported for every task with uncorrected CIs (descriptive). + +**Decision rule**: VALIDATED = primary CI lower bound > 0 and no Holm-supported regression. NOT VALIDATED = any Holm-supported regression, or primary point estimate ≤ 0. INCONCLUSIVE = no supported regression and positive primary point estimate, but primary CI includes 0 (or fewer than 5 test repositories with comment2context queries). + +**Bootstrap**: resample repositories with replacement, 10,000 resamples, seed 20261109. + +### 2.1 Split + +| Task | Repositories dev / test | Queries dev / test | +| :--- | :---: | :---: | +| comment2context (primary) | 11 / 7 | 46 / 34 | +| code2test | 8 / 4 | 72 / 34 | +| trace2code | 3 / 4 | 14 / 87 | +| edit2ripple | 7 / 3 | 34 / 24 | +| **Total** | **15 / 10** | **166 / 179** | + +Test repositories: `astral-sh/ruff`, `etcd-io/etcd`, `gin-gonic/gin`, `microsoft/playwright`, `mockito/mockito`, `numpy/numpy`, `pallets/click`, `pytest-dev/pytest`, `scrapy/scrapy`, `tox-dev/tox`. + +As pre-registered, stratification balanced **repository** coverage per task, not query counts. One consequence: only 14 trace2code queries (3 repositories) are in development, so the development trace2code signal is weak. The split was not changed after it was generated. + +## 3. Development results (exploratory; used only to select the penalty) + +Grid {0.5, 0.25, 0.0}, all on development repositories only (166 queries, 15 repositories). Differences are filter ON − OFF. + +| Task (queries / repos) | MRR Δ (p = 0.5) | Recall@20 Δ (p = 0.5) | Docs in top 10, OFF → ON | +| :--- | :---: | :---: | :---: | +| comment2context (46 / 11) | +0.0094 | +0.0580 | 1.85 → 0.00 | +| code2test (72 / 8) | +0.0364 | +0.0000 | 0.94 → 0.06 | +| trace2code (14 / 3) | +0.0141 | +0.0357 | 1.71 → 0.00 | +| edit2ripple (34 / 7) | +0.0026 | +0.0294 | 0.47 → 0.00 | +| Overall (166 / 15) | +0.0201 | +0.0251 | 1.16 → 0.02 | + +Penalties 0.25 and 0.0 produced the same comment2context Recall@20 gain (+0.0580) and near-identical results otherwise: at 0.5, documentation already leaves the top 10. All three met the development guardrail (no mean loss beyond −0.02). By the pre-registered tie rule (larger, milder penalty), **0.5 was selected and frozen**. This equals the existing default value of `doc_filter_penalty`, so no parameter was tuned away from its default. Full development contrasts with CIs: `dev_results.json`. + +## 4. Held-out confirmatory test (run once, frozen penalty 0.5) + +Before running, the script verified the split hash and the frozen configuration hash. 179 / 179 test queries (10 repositories), 0 duplicates, every row from a test repository. The control arm reproduces Phase 1 whole-file results exactly (179 / 179). + +### 4.1 Primary criterion + +| comment2context Recall@20 (34 queries, 7 repositories) | OFF | ON | Δ | W / T / L | 95% CI | +| :--- | :---: | :---: | :---: | :---: | :---: | +| Test | 0.6373 | 0.6373 | **0.0000** | 0 / 34 / 0 | [0.0000, 0.0000] | + +**The primary criterion is not met.** The filter did not change whether any comment2context gold file appears in the top 20 of any held-out query. + +### 4.2 Guardrails (Holm-adjusted, one-sided regression tests) + +| Hypothesis | Δ (ON − OFF) | 95% CI | p (one-sided) | p (Holm) | Regression supported | +| :--- | :---: | :---: | :---: | :---: | :---: | +| comment2context MRR | +0.0299 | [+0.0068, +0.0765] | 1.000 | 1.000 | no | +| code2test MRR | +0.0004 | [+0.0000, +0.0009] | 1.000 | 1.000 | no | +| code2test Recall@20 | 0.0000 | [0.0000, 0.0000] | 1.000 | 1.000 | no | +| trace2code MRR | +0.0778 | [+0.0099, +0.0875] | 1.000 | 1.000 | no | +| trace2code Recall@20 | +0.0211 | [0.0000, +0.0322] | 1.000 | 1.000 | no | +| edit2ripple MRR | +0.0008 | [0.0000, +0.0033] | 1.000 | 1.000 | no | +| edit2ripple Recall@20 | 0.0000 | [0.0000, 0.0000] | 1.000 | 1.000 | no | + +No guardrail shows a regression; no test query got worse on any full-ranking metric. + +### 4.3 All tasks and metrics (test; uncorrected 95% CIs, descriptive except where stated above) + +| Task (queries / repos) | MRR Δ | Recall@5 Δ | Recall@10 Δ | Recall@20 Δ | +| :--- | :---: | :---: | :---: | :---: | +| comment2context (34 / 7) | +0.0299 [+0.0068, +0.0765] | +0.0147 [0.0000, +0.0682] | +0.0539 [+0.0090, +0.1842] | 0.0000 [0.0000, 0.0000] | +| code2test (34 / 4) | +0.0004 [0.0000, +0.0009] | 0.0000 | +0.0147 [0.0000, +0.1250] | 0.0000 | +| trace2code (87 / 4) | +0.0778 [+0.0099, +0.0875] | +0.0843 [0.0000, +0.1023] | +0.0594 [+0.0111, +0.2000] | +0.0211 [0.0000, +0.0322] | +| edit2ripple (24 / 3) | +0.0008 [0.0000, +0.0033] | 0.0000 | 0.0000 | 0.0000 | +| **Overall (179 / 10)** | **+0.0437 [+0.0098, +0.0623]** | +0.0438 [0.0000, +0.0640] | +0.0419 [+0.0167, +0.1026] | +0.0102 [0.0000, +0.0182] | + +Absolute test values (OFF → ON): comment2context MRR 0.2465 → 0.2764; trace2code MRR 0.2873 → 0.3651; overall MRR 0.2832 → 0.3269. Per-query reciprocal ranks and gold positions: `test/` shard files. + +### 4.4 Documentation in rankings, packed context, latency (test) + +| Task | Docs in top 5 / 10 / 20, OFF → ON | Packed gold coverage, OFF → ON | Queries with ≥1 gold packed, OFF → ON | Utilisation | +| :--- | :---: | :---: | :---: | :---: | +| comment2context | 0.74 / 1.74 / 3.56 → 0.03 / 0.03 / 0.18 | 45.6% → 51.0% | 67.6% → 76.5% | 99.3% | +| code2test | 0.12 / 0.18 / 0.38 → 0 / 0 / 0 | 62.9% → 62.9% | 67.6% → 67.6% | 99.4% | +| trace2code | 1.34 / 2.30 / 3.92 → 0.01 / 0.21 / 0.54 | 73.6% → 81.4% | 77.0% → 83.9% | 99.7% | +| edit2ripple | 0.12 / 0.33 / 0.83 → 0 / 0 / 0 | 79.5% → 79.5% | 91.7% → 91.7% | 99.6% | +| Overall | 0.83 / 1.53 / 2.77 → 0.01 / 0.11 / 0.30 | 67.0% → 71.9% | 75.4% → 80.4% | 99.5% | + +**Latency is not a valid comparison in this design.** Both arms run on the same engine instance in a fixed order (OFF first), so the ON arm benefits from caches warmed by the OFF call (mean 546 ms vs 93 ms). The arms were not counterbalanced. The filter itself is a single pass over file paths and should be negligible, but that was not measured here. + +## 5. Decision + +### **B. NOT VALIDATED** + +The pre-registered primary criterion (a supported positive change in comment2context Recall@20 on held-out repositories) failed: the observed change was exactly zero. No guardrail regression was found. + +Secondary observations, **not** usable as validation because they were not the pre-registered primary endpoint (exploratory for future work): +- The filter improved comment2context MRR (+0.030, CI excludes zero) and overall MRR (+0.044, CI excludes zero) on held-out repositories, with **zero** per-query losses on any full-ranking metric across 179 queries. +- The effect is a reordering **within** the top ranks: documentation leaves the top 10 (1.53 → 0.11 per query), lifting gold files that were already near the top, without moving gold files across the rank-20 boundary. + +## 6. Gold documentation and false negatives + +| Split | Gold files | Gold documentation files | Gold docs pushed out of top 20 | Gold docs dropped from packed context | Non-doc gold dropped from packed context | Non-doc gold entering top 20 | +| :--- | :---: | :---: | :---: | :---: | :---: | :---: | +| Development (p = 0.5) | 252 | **4** | **1** | **1** | 0 | 7 | +| Test (p = 0.5) | 262 | **0** | 0 | 0 | **1** | 3 | + +- **The filter does harm relevant documentation.** One of the 4 development gold documentation files left both the top 20 and the packed context. The test split contained no gold documentation, so the held-out test **cannot** measure this risk. The "no comment2context gold is documentation" observation from Phase 0 does not generalise to all tasks. +- **Test false negative** (`false_negative_examples.json`): trace2code, `gin-gonic/gin`, gold `context.go` moved from rank 11 to rank 10 under the filter but was no longer packed. Promoting other non-documentation files changed CUD-Pack's representation choices. This is a packing interaction, not a ranking loss. + +## 7. Limitations + +- **Small held-out sample**: 7 comment2context repositories (34 queries) and 3–4 repositories for the other tasks. Clustered percentile CIs with this few clusters are approximate, and degenerate to [0, 0] when every query ties. +- **Query imbalance from repository-level stratification**: trace2code has 14 development vs 87 test queries. +- **The test split contains no gold documentation**, so harm to relevant documentation is measured only on development data (4 files). +- **The documentation predicate is broader than documentation** (`.txt`, `.github/`, any path containing `doc/`). +- **Latency** is confounded by arm order (§4.4). +- A single benchmark (ARB v2) of structured, non-conversational queries; task labels passed explicitly. +- The held-out set has now been used once. Any follow-up confirmatory study (for example with MRR as a pre-registered primary endpoint) needs **new** held-out data, not these repositories. + +## 8. Production changes + +None. No file under `reponeedle/` was modified (verified by SHA-256 against the pre-milestone fingerprint). The filter remains opt-in and disabled by default. + +## 9. Artifacts + +`protocol.json` (+ `.sha256`), `split.json` (+ `split.sha256`), `selected_filter_config.json` (+ `.sha256`), `dev/` and `test/` per-query results (metrics, reciprocal ranks, gold positions, top-20 rankings, packed stats, latencies), `dev_results.json`, `test_results.json` (contrasts, CIs, Holm results, decision), `false_negative_examples.json`, `run_metadata.json`. diff --git a/reports/experimental/phase2_documentation_filter/dev/shard_0_of_4.jsonl b/reports/experimental/phase2_documentation_filter/dev/shard_0_of_4.jsonl new file mode 100644 index 0000000..0be70bd --- /dev/null +++ b/reports/experimental/phase2_documentation_filter/dev/shard_0_of_4.jsonl @@ -0,0 +1,45 @@ +{"sample_id": "bf47308900fafdff5ca96ecd", "task_type": "comment2context", "repo": "HypothesisWorks/hypothesis", "base_commit": "c1d70b89c5d586660e1b21fdf422a10fd14e9d6c", "phase": "dev", "gold_files": ["hypothesis-python/src/hypothesis/core.py", 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0000000..453dc24 --- /dev/null +++ b/reports/experimental/phase2_documentation_filter/protocol.json @@ -0,0 +1,77 @@ +{ + "milestone": "2B documentation-filter held-out validation (EXPERIMENTAL; not an official ARB score)", + "framework": "Phase 1 product-aligned evaluation: file unit, whole-file input, full ranking; packed context separate", + "treatment": "AblationConfig(enable_doc_filter=True, doc_filter_penalty=p) passed per call to retrieve()", + "control": "AblationConfig() (enable_doc_filter=False) passed per call to retrieve()", + "doc_predicate": "engine.py legacy ranker: path contains /doc/, /docs/, doc/, docs/, documentation/, guide/, tutorial/, .github/ OR ends with .md, .rst, .txt, .adoc, .html (case-insensitive)", + "split": { + "unit": "repository", + "test_repositories": 10, + "dev_repositories": 15, + "seed": "reponeedle-2B-split-v1", + "algorithm": "For attempt a = 0, 1, ...: shuffle the sorted repository list with random.Random(f'{seed}:{a}'); test = first 10. Accept the first attempt where, for every task with n repositories, the test count is within +-1 of round(0.4*n) and both splits contain >= 1 repository with that task.", + "inputs_used": "repository ids and task coverage (presence of >=1 gold-labelled query per task) only" + }, + "primary": { + "task": "comment2context", + "metric": "recall@20", + "contrast": "filter ON - filter OFF on TEST repositories", + "success": "paired repository-clustered 95% percentile CI lower bound > 0" + }, + "guardrails": { + "hypotheses": [ + [ + "comment2context", + "mrr" + ], + [ + "code2test", + "mrr" + ], + [ + "code2test", + "recall@20" + ], + [ + "trace2code", + "mrr" + ], + [ + "trace2code", + "recall@20" + ], + [ + "edit2ripple", + "mrr" + ], + [ + "edit2ripple", + "recall@20" + ] + ], + "test": "one-sided regression test: bootstrap p = (1 + #{resampled mean diff >= 0}) / (B + 1)", + "correction": "Holm-Bonferroni over the 7 guardrail hypotheses, family-wise alpha = 0.05", + "note": "Recall@5 and Recall@10 for every task are reported with uncorrected CIs (descriptive)" + }, + "bootstrap": { + "type": "paired, repository-clustered percentile", + "resamples": 10000, + "seed": 20261109, + "ci": 0.95 + }, + "dev_grid": [ + 0.5, + 0.25, + 0.0 + ], + "dev_selection_rule": "Among grid values whose DEV mean (ON - OFF) for comment2context MRR and for MRR and Recall@20 of every other task is >= -0.02, choose the one maximising DEV mean comment2context Recall@20 gain, provided it is > 0; ties -> larger penalty (milder). If none qualifies, freeze the existing default penalty 0.5 and record the dev failure.", + "decision_rule": { + "VALIDATED": "primary CI lower bound > 0 AND no Holm-supported guardrail regression", + "NOT VALIDATED": "any Holm-supported guardrail regression, OR primary point estimate <= 0", + "INCONCLUSIVE": "no Holm-supported regression, primary point estimate > 0, but primary CI includes 0; or fewer than 5 test repositories contain comment2context queries" + }, + "budget_tokens": 8000, + "task_label_passed_to_engine": true, + "split_sha256": "6b8b0322ef70d5ffbd072c8a10d7b0154bc166dc3650df85c1cb30a154ba5bb4", + "registered_at": "2026-11-09T19:12:00" +} \ No newline at end of file diff --git a/reports/experimental/phase2_documentation_filter/protocol.sha256 b/reports/experimental/phase2_documentation_filter/protocol.sha256 new file mode 100644 index 0000000..437bb23 --- /dev/null +++ b/reports/experimental/phase2_documentation_filter/protocol.sha256 @@ -0,0 +1 @@ +5151f33622f9694828256337a897a13086d14cb93fe5437e604d8bc04196ac57 protocol.json diff --git a/reports/experimental/phase2_documentation_filter/run_metadata.json b/reports/experimental/phase2_documentation_filter/run_metadata.json new file mode 100644 index 0000000..67161f3 --- /dev/null +++ b/reports/experimental/phase2_documentation_filter/run_metadata.json @@ -0,0 +1,7 @@ +{ + "git_head": "82d7fb3aa3c0868564e1f4b16c228359db8678d1", + "python": "3.12.10", + "numpy": "2.5.2", + "platform": "Windows-11-10.0.26200-SP0", + "reported_at": "2026-11-09T19:37:28" +} \ No newline at end of file diff --git a/reports/experimental/phase2_documentation_filter/selected_filter_config.json b/reports/experimental/phase2_documentation_filter/selected_filter_config.json new file mode 100644 index 0000000..c066a69 --- /dev/null +++ b/reports/experimental/phase2_documentation_filter/selected_filter_config.json @@ -0,0 +1,21 @@ +{ + "enable_doc_filter": true, + "doc_filter_penalty": 0.5, + "status": "selected by dev rule", + "dev_eligible": [ + { + "penalty": 0.5, + "dev_c2c_recall20_gain": 0.05797101449275362 + }, + { + "penalty": 0.25, + "dev_c2c_recall20_gain": 0.05797101449275362 + }, + { + "penalty": 0.0, + "dev_c2c_recall20_gain": 0.05797101449275362 + } + ], + "split_sha256": "6b8b0322ef70d5ffbd072c8a10d7b0154bc166dc3650df85c1cb30a154ba5bb4", + "frozen_at": "2026-11-09T19:32:56" +} \ No newline at end of file diff --git a/reports/experimental/phase2_documentation_filter/selected_filter_config.sha256 b/reports/experimental/phase2_documentation_filter/selected_filter_config.sha256 new file mode 100644 index 0000000..d3868d7 --- /dev/null +++ b/reports/experimental/phase2_documentation_filter/selected_filter_config.sha256 @@ -0,0 +1 @@ +30343e918194787a100251a3407d4d6d2dd69f51c4cbe9d7be7b8d987e9fa814 selected_filter_config.json diff --git a/reports/experimental/phase2_documentation_filter/split.json b/reports/experimental/phase2_documentation_filter/split.json new file mode 100644 index 0000000..bd9cf78 --- /dev/null +++ b/reports/experimental/phase2_documentation_filter/split.json @@ -0,0 +1,274 @@ +{ + "accepted_attempt": 0, + "algorithm": "For attempt a = 0, 1, ...: shuffle the sorted repository list with random.Random(f'{seed}:{a}'); test = first 10. Accept the first attempt where, for every task with n repositories, the test count is within +-1 of round(0.4*n) and both splits contain >= 1 repository with that task.", + "assignment": { + "HypothesisWorks/hypothesis": "dev", + "astral-sh/ruff": "test", + "caddyserver/caddy": "dev", + "clap-rs/clap": "dev", + "eslint/eslint": "dev", + "etcd-io/etcd": "test", + "fastapi/fastapi": "dev", + "gin-gonic/gin": "test", + "huggingface/diffusers": "dev", + "huggingface/transformers": "dev", + "ipython/ipython": "dev", + "microsoft/playwright": "test", + "mockito/mockito": "test", + "numpy/numpy": "test", + "pallets/click": "test", + "pydantic/pydantic": "dev", + "pypa/pip": "dev", + "pytest-dev/pytest": "test", + "python/mypy": "dev", + "scrapy/scrapy": "test", + "spring-projects/spring-boot": "dev", + "tokio-rs/tokio": "dev", + "tox-dev/tox": "test", + "vitejs/vite": "dev", + "vuejs/core": "dev" + }, + "queries_per_task": { + "code2test": { + "dev": 72, + "test": 34 + }, + "comment2context": { + "dev": 46, + "test": 34 + }, + "edit2ripple": { + "dev": 34, + "test": 24 + }, + "trace2code": { + "dev": 14, + "test": 87 + } + }, + "query_counts_not_used_for_selection": { + "HypothesisWorks/hypothesis": { + "comment2context": 1 + }, + "astral-sh/ruff": { + "comment2context": 6 + }, + "caddyserver/caddy": { + "edit2ripple": 6, + "trace2code": 7 + }, + "clap-rs/clap": { + "code2test": 4, + "edit2ripple": 5, + "trace2code": 1 + }, + "eslint/eslint": { + "comment2context": 1 + }, + "etcd-io/etcd": { + "code2test": 15, + "comment2context": 16, + "edit2ripple": 6, + "trace2code": 4 + }, + "fastapi/fastapi": { + "code2test": 3 + }, + "gin-gonic/gin": { + "code2test": 15, + "edit2ripple": 17, + "trace2code": 56 + }, + "huggingface/diffusers": { + "code2test": 6, + "comment2context": 9, + "edit2ripple": 1 + }, + "huggingface/transformers": { + "code2test": 15, + "comment2context": 13, + "edit2ripple": 8 + }, + "ipython/ipython": { + "comment2context": 2 + }, + "microsoft/playwright": { + "comment2context": 4 + }, + "mockito/mockito": { + "code2test": 1 + }, + "numpy/numpy": { + "comment2context": 1 + }, + "pallets/click": { + "trace2code": 26 + }, + "pydantic/pydantic": { + "comment2context": 1 + }, + "pypa/pip": { + "comment2context": 5 + }, + "pytest-dev/pytest": { + "code2test": 3, + "comment2context": 2, + "edit2ripple": 1, + "trace2code": 1 + }, + "python/mypy": { + "comment2context": 1 + }, + "scrapy/scrapy": { + "comment2context": 3 + }, + "spring-projects/spring-boot": { + "code2test": 5, + "edit2ripple": 3 + }, + "tokio-rs/tokio": { + "code2test": 15, + "comment2context": 11, + "edit2ripple": 6, + "trace2code": 6 + }, + "tox-dev/tox": { + "comment2context": 2 + }, + "vitejs/vite": { + "code2test": 19, + "comment2context": 1, + "edit2ripple": 5 + }, + "vuejs/core": { + "code2test": 5, + "comment2context": 1 + } + }, + "repositories_per_task": { + "code2test": { + "dev": 8, + "target_test": 5, + "test": 4, + "total": 12 + }, + "comment2context": { + "dev": 11, + "target_test": 7, + "test": 7, + "total": 18 + }, + "edit2ripple": { + "dev": 7, + "target_test": 4, + "test": 3, + "total": 10 + }, + "trace2code": { + "dev": 3, + "target_test": 3, + "test": 4, + "total": 7 + } + }, + "seed": "reponeedle-2B-split-v1", + "task_coverage": { + "HypothesisWorks/hypothesis": [ + "comment2context" + ], + "astral-sh/ruff": [ + "comment2context" + ], + "caddyserver/caddy": [ + "edit2ripple", + "trace2code" + ], + "clap-rs/clap": [ + "code2test", + "edit2ripple", + "trace2code" + ], + "eslint/eslint": [ + "comment2context" + ], + "etcd-io/etcd": [ + "code2test", + "comment2context", + "edit2ripple", + "trace2code" + ], + "fastapi/fastapi": [ + "code2test" + ], + "gin-gonic/gin": [ + "code2test", + "edit2ripple", + "trace2code" + ], + "huggingface/diffusers": [ + "code2test", + "comment2context", + "edit2ripple" + ], + "huggingface/transformers": [ + "code2test", + "comment2context", + "edit2ripple" + ], + "ipython/ipython": [ + "comment2context" + ], + "microsoft/playwright": [ + "comment2context" + ], + "mockito/mockito": [ + "code2test" + ], + "numpy/numpy": [ + "comment2context" + ], + "pallets/click": [ + "trace2code" + ], + "pydantic/pydantic": [ + "comment2context" + ], + "pypa/pip": [ + "comment2context" + ], + "pytest-dev/pytest": [ + "code2test", + "comment2context", + "edit2ripple", + "trace2code" + ], + "python/mypy": [ + "comment2context" + ], + "scrapy/scrapy": [ + "comment2context" + ], + "spring-projects/spring-boot": [ + "code2test", + "edit2ripple" + ], + "tokio-rs/tokio": [ + "code2test", + "comment2context", + "edit2ripple", + "trace2code" + ], + "tox-dev/tox": [ + "comment2context" + ], + "vitejs/vite": [ + "code2test", + "comment2context", + "edit2ripple" + ], + "vuejs/core": [ + "code2test", + "comment2context" + ] + } +} \ No newline at end of file diff --git a/reports/experimental/phase2_documentation_filter/split.sha256 b/reports/experimental/phase2_documentation_filter/split.sha256 new file mode 100644 index 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"packed_utilisation": 0.995546875, + "latency_ms_mean": 96.5172166688717, + "latency_ms_p50": 19.433700013905764 + } + } + }, + "gold_effects": { + "gold_nondoc:gained_top20": 3, + "gold_nondoc:lost_packed": 1, + "gold_nondoc:total": 262 + }, + "n_test_queries": 179, + "n_test_repositories": 10 +} \ No newline at end of file diff --git a/reports/experimental/phase3_lexical_retrieval/REPORT.md b/reports/experimental/phase3_lexical_retrieval/REPORT.md new file mode 100644 index 0000000..643a2ba --- /dev/null +++ b/reports/experimental/phase3_lexical_retrieval/REPORT.md @@ -0,0 +1,107 @@ +# Milestone 2C: Lexical Retrieval Forensics and Development Ablations + +> [!IMPORTANT] +> **EXPERIMENTAL. Development evidence only. No confirmatory result.** All experiments used the 15 Milestone 2B **development** repositories (previously used development data). The 10 Milestone 2B held-out repositories were never loaded (asserted in code). **No fresh confirmatory data exists locally**: every ARB split on disk (v0.1, v2 positives, v2 selective, abstention) draws on the same 25 repositories. No production code was changed. Official ARB v2 results and Phases 0–2 are unchanged. + +**Scripts**: `benchmarks/run_lexical_forensics.py` · **Tests**: `tests/test_lexical_forensics.py` · **Protocol SHA-256**: `dd0860bac5f0ca016a44156b84441696f547417ba0a465f80468dafa83aa0f11` (registered before any variant result) · **Framework**: Phase 1 product-aligned evaluation (whole-file input, file unit, full ranking) + +--- + +## 1. Conclusion + +**NOT VALIDATED.** No lexical change met the pre-registered development-support criterion, so nothing was implemented and nothing was eligible for confirmation. Independently of that, **fresh confirmatory data is unavailable**, so no confirmatory claim would have been possible in any case. + +The forensic analysis did identify the **root cause** of the lexical deficit (§3): RepoNeedle's `BM25Index` **ignores query term frequency**. That single factor closes about 79% of the comment2context lexical gap to BM25 on development data, but it also harms trace2code. The pre-registered guardrail excluded it from the hybrid stage. It is the recommended subject of the next pre-registered study (§9). + +## 2. Lexical pipeline (traced from code) + +| Component | RepoNeedle (`reponeedle/core/lexical.py`, `tokenizer.py`) | ARB BM25 (Phase 1 comparator) | +| :--- | :--- | :--- | +| Unit / input | File; whole-file text (benchmark reconstruction) | File; same whole-file text | +| Document fields | Path tokens × 2.5 (deduplicated per path), symbol tokens × 2.0, body tokens × 1.0, summed into one weighted tf per term | Path + text concatenated once | +| Symbol field in the benchmark | **Empty**: harness documents carry no symbols, and polyglot extraction is off by default (real `from_directory` fills Python symbols via AST) | n/a | +| Tokenizer | `[A-Za-z0-9_]+` words; for each word the raw lowercase form **and** its identifier pieces (camel/snake split). A plain word yields its own token **twice**. Stopwords incl. `self`, `return`, `none`, `true`, `false` | camelCase split, `[A-Za-z0-9]+`, lowercase; one token per piece; no stopwords | +| Query handling | Whole JSON string tokenized; **query term counts are computed but not used** (each distinct term contributes once) | Each term's contribution × its query count | +| BM25 | k1 = 1.5, b = 0.75, global average length over body + path tokens, RSJ idf `ln((N−df+0.5)/(df+0.5)+1)`, scores max-normalised to [0, 1] for fusion | k1 = 1.5, b = 0.75, same idf | +| Use in ranking | Lexical score fused with semantic (+ structural, workflow) scores by task-specific router weights | n/a | + +## 3. Development forensics (lexical-only ranking, 166 queries / 15 repositories) + +Baseline reproduction: lexical-only L0 equals the Phase 0 whole-file lexical-only results on **91 / 91** overlapping development queries. The hybrid baseline equals Phase 1 whole-file results on **166 / 166**. + +### 3.1 One-factor ablations (comment2context Recall@20; repository-clustered 95% CI) + +| Factor | Variant | comment2context R@20 (L0 = 0.3732; BM25 = 0.5652) | Δ vs L0 [CI] | Worst task MRR/R@20 change | +| :--- | :--- | :---: | :---: | :---: | +| **Query term frequency** | L4: multiply by query count | **0.5254** | **+0.1522 [+0.051, +0.201]** | **−0.0555** (trace2code MRR) | +| Token duplication | L5: each token once per word | 0.4312 | +0.0580 [+0.000, +0.144] | −0.0025 | +| JSON key tokens | L6: score JSON leaf values only | 0.3949 | +0.0217 [−0.022, +0.089] | −0.0046 | +| Length normalisation | L2: b = 0.4 | 0.4058 | +0.0326 [−0.024, +0.153] | −0.0301 | +| Length normalisation | L3: b = 0.9 | 0.3659 | −0.0072 [−0.020, +0.000] | −0.0121 | +| Field weighting | L1: path weight 2.5 → 1.0 | 0.3732 | 0.0000 | −0.0139 | + +### 3.2 Field contribution + +| Diagnostic | comment2context MRR / R@20 | Overall MRR / R@20 | +| :--- | :---: | :---: | +| Production (all fields) | 0.1731 / 0.3732 | 0.1551 / 0.3961 | +| Path field only | 0.0656 / 0.1377 | 0.0586 / 0.1807 | +| Body field only | 0.1832 / 0.4022 | 0.1561 / 0.3951 | + +The body field carries almost all of the lexical signal; the ×2.5 path weight neither helps nor causes the deficit (L1: no change). The symbol field is empty in this benchmark, so its ×2.0 weight was not exercised. + +### 3.3 Structured query tokenization (`forensics_query_and_length.json`) + +| Task (dev) | Tokens, RepoNeedle vs ARB (median) | Unique / total | Max repeats of one term | JSON key share | Largest fields | +| :--- | :---: | :---: | :---: | :---: | :--- | +| comment2context | 139 vs 92 | 0.48 | 7 | 12.2% | review_comment 34%, diff_hunk_context 24%, pr_title 9%, given_file 8%, path 8% | +| code2test | 204 vs 135 | 0.41 | 10 | 6.0% | pr_body 67% | +| trace2code | 868 vs 540 | 0.21 | 58 | 1.3% | failure_excerpt 92% | +| edit2ripple | 1,540 vs 1,020 | 0.18 | 184 | 0.5% | anchor_diff 68%, intent 16% | + +- All development queries are JSON objects; key names contribute 12% of comment2context query tokens (small, inconsistent benefit from dropping them: L6). +- Token duplication inflates RepoNeedle's query token counts by about 1.5× relative to ARB. +- **Why query term frequency is task-specific**: in comment2context, repetition is informative (an identifier repeated across the review comment, diff hunk and path is likely the subject). In CI failure excerpts and diffs, a few terms repeat 58–184 times, so weighting by raw query count amplifies log and diff noise, consistent with L4's trace2code MRR loss. + +### 3.4 Length normalisation + +Gold files are long (median length percentile 0.87–0.95 across tasks), and so are the top-10 non-gold files (6.5–10× the corpus median). Neither b = 0.4 nor b = 0.9 produced a consistent benefit. Length normalisation is **not** a cause of the deficit. + +## 4. Root cause + +The comment2context lexical deficit is caused mainly by **ignoring query term frequency** (L4 recovers +0.152 of the 0.192 Recall@20 gap on development data), with a smaller contribution from **duplicate token counting** (L5, +0.058). Path field weighting and length normalisation are not causes. JSON key tokens contribute marginally. + +## 5. Stage 1 → stage 2 (pre-registered rules) + +**Stage 1 rule** (lexical-only): candidate if comment2context Recall@20 gain > 0 and no task's MRR or Recall@20 mean change < −0.02. Candidates: **L5** and **L6**, plus their combination. **L4 was excluded** (trace2code MRR −0.0555, from 14 queries in 3 repositories); L2 was excluded (−0.0301). + +**Stage 2** (full RepoNeedle hybrid, development data; Δ vs hybrid baseline): + +| Arm | c2c R@20 Δ [CI] | c2c MRR Δ | code2test MRR Δ | trace2code MRR Δ | edit2ripple MRR Δ [CI] | Eligible | +| :--- | :---: | :---: | :---: | :---: | :---: | :---: | +| L5 no duplicate tokens | +0.0290 [+0.000, +0.068] | −0.0126 | −0.0054 | −0.0015 | −0.0040 | yes | +| L6 JSON values only | +0.0471 [+0.000, +0.098] | +0.0030 | **−0.0349** | +0.0033 | −0.0038 | **no** (guardrail) | +| **L5 + L6 combined** | **+0.0507 [+0.000, +0.107]** | +0.0099 | +0.0026 | +0.0005 | −0.0085 [−0.016, −0.001] | yes, **selected** | + +**Stage 2 rule**: select the eligible arm with the largest comment2context Recall@20 gain (the combined arm); it is "dev-supported" only if that CI excludes zero. The combined arm's CI lower bound is **+0.000**, so it is **not dev-supported**. By the pre-registered rule, **no implementation and no default change** were made. + +## 6. Implementation + +None. `reponeedle/` is byte-identical to its pre-Milestone-2A fingerprint. All variants exist only as harness subclasses in `benchmarks/run_lexical_forensics.py`. + +## 7. Confirmatory data + +Not available. A valid confirmation requires fresh repositories disjoint from all 25 ARB v2 repositories, independently labelled for all four tasks (in particular comment2context), split at repository level with a recorded hash, and a protocol registered before treatment results are examined. The previous 10 held-out repositories were not used. + +## 8. Limitations + +- Development data was previously used (Milestone 2B), and development samples are small for some tasks (trace2code: 14 queries, 3 repositories). +- The L4 exclusion rests on that small trace2code sample. +- Only the pre-registered matrix was tested; no capped or saturated query term frequency variant was evaluated. +- Benchmark queries are structured JSON; task labels are passed explicitly; whole files are reconstructions from ARB chunks. + +## 9. Recommendation + +1. **Collect fresh confirmatory data first**: new repositories (disjoint from the 25 ARB v2 repositories) with independently established comment2context labels and coverage of the other three tasks. Without it, no lexical improvement can be confirmed. +2. **Pre-register a focused query-term-frequency study** on development data: saturated or capped query term frequency (for example `1 + log(count)` or `min(count, k)`), alone and combined with duplicate-token removal. Primary endpoint: comment2context Recall@20; guardrail emphasis on trace2code and edit2ripple, where queries are highly repetitive. +3. Keep all lexical behaviour unchanged by default until a confirmatory study supports a change. diff --git a/reports/experimental/phase3_lexical_retrieval/dev_selection.json b/reports/experimental/phase3_lexical_retrieval/dev_selection.json new file mode 100644 index 0000000..2b7ec58 --- /dev/null +++ b/reports/experimental/phase3_lexical_retrieval/dev_selection.json @@ -0,0 +1,19 @@ +{ + "selected_arm": "H_C_combined", + "dev_supported": false, + "eligible": [ + { + "arm": "H_L5_no_duplicate_tokens", + "c2c_recall20_gain": 0.02898550724637681 + }, + { + "arm": "H_C_combined", + "c2c_recall20_gain": 0.05072463768115942 + } + ], + "selected_config": { + "dedupe": true, + "json_values_only": true + }, + "note": "development evidence only (previously used development repositories); not confirmatory" +} \ No newline at end of file diff --git a/reports/experimental/phase3_lexical_retrieval/dev_selection.sha256 b/reports/experimental/phase3_lexical_retrieval/dev_selection.sha256 new file mode 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"L5_no_duplicate_tokens": { + "dedupe": true + }, + "L6_json_values_only": { + "json_values_only": true + } + }, + "variant_semantics": { + "path_weight / b": "existing BM25Index constructor parameters", + "query_tf": "multiply each query term's BM25 contribution by its query count (ARB BM25 behaviour)", + "dedupe": "emit each token at most once per source word (current tokenizer emits plain words twice: raw form and identical piece)", + "json_values_only": "if the query parses as a JSON object, score only its leaf values (key names dropped); otherwise unchanged" + }, + "diagnostics": { + "D_path_field_only": { + "body_weight": 0.0 + }, + "D_body_field_only": { + "path_weight": 0.0 + } + }, + "stage1_rule": "Lexical-only, dev. A variant is a hybrid candidate if its dev mean (variant - L0) comment2context Recall@20 is > 0 and its dev mean for MRR and Recall@20 of every task is >= -0.02. At most the 3 candidates with the largest comment2context Recall@20 gain go to stage 2, plus 'C_combined' = all candidates applied together when >= 2 qualify.", + "stage2_rule": "Full hybrid, dev. Select the arm with the largest dev mean comment2context Recall@20 gain over the hybrid baseline among arms whose dev mean MRR and Recall@20 for every task (and comment2context MRR) is >= -0.02. The selection is 'dev-supported' only if its repository-clustered 95% CI for comment2context Recall@20 excludes zero (positive). If no arm is dev-supported, no default change is made; any implementation stays opt-in.", + "bootstrap": { + "type": "paired, repository-clustered percentile", + "resamples": 10000, + "seed": 20261109 + }, + "confirmatory_requirement": "Fresh repositories disjoint from all 25 ARB v2 repositories, independently labelled, covering all four tasks, split at repository level with a recorded hash, protocol registered before treatment results. If unavailable: no confirmatory claim (INCONCLUSIVE).", + "registered_at": "2026-11-09T19:47:28" +} \ No newline at end of file diff --git a/reports/experimental/phase3_lexical_retrieval/protocol.sha256 b/reports/experimental/phase3_lexical_retrieval/protocol.sha256 new file mode 100644 index 0000000..5702227 --- /dev/null +++ b/reports/experimental/phase3_lexical_retrieval/protocol.sha256 @@ -0,0 +1 @@ +dd0860bac5f0ca016a44156b84441696f547417ba0a465f80468dafa83aa0f11 protocol.json diff --git a/reports/experimental/phase3_lexical_retrieval/stage1_results.json b/reports/experimental/phase3_lexical_retrieval/stage1_results.json new file mode 100644 index 0000000..9e28f27 --- /dev/null +++ b/reports/experimental/phase3_lexical_retrieval/stage1_results.json @@ -0,0 +1,2448 @@ +{ + "contrasts_vs_L0": { + "L1_path_weight_1.0": { + "overall": { + "n_queries": 166, + "n_repositories": 15, + "mrr": { + "a": 0.1544872253229508, + "b": 0.15511622444543496, + "mean_diff": -0.0006289991224841428, + 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**Behaviour change**: the experimental selective-abstention heuristic is now **disabled by default**. + +## New features + +### MCP server (optional) +- Install: `pip install "reponeedle[mcp]"`. Run: `reponeedle-mcp --repo /path/to/project` (or `python -m reponeedle.mcp`). +- Tools: `reponeedle_retrieve` (context retrieval within a token budget; optional `enable_experimental_abstention`, default `false`), `reponeedle_change_impact` (files and tests related to modified anchor files), `reponeedle_inspect` (index metadata, indexing diagnostics, abstention status). +- The repository is indexed once at startup; restart the server to refresh. Path arguments are confined to the repository root. +- STDIO transport only. Tested with the MCP Python SDK and a JSON-RPC STDIO session; not yet validated in a live Claude Code or Cursor session. +- Without the extra, the core package is unaffected and `reponeedle-mcp` prints installation instructions. + +### CLI +- `--json`: emits one JSON document on stdout (`schema_version` 1.2.0) with selected files, scores, tiers, token counts, `abstention` and `indexing` objects. Diagnostics go to stderr only. +- `--include-context`: include the rendered prompt context in the JSON output. +- `--anchors PATH [PATH ...]`: seed/anchor files for change-impact and ripple queries; paths are validated and must stay inside the repository. +- `--enable-experimental-abstention`: opt in to the experimental abstention heuristic. +- `--max-file-size-bytes BYTES`: per-file indexing limit (default 500,000). + +### Python API +- `RepoNeedleEngine.retrieve(..., anchor_files=[...])` and `Query(anchor_files=[...])`. +- `RepoNeedleEngine.from_directory(..., max_file_size_bytes=500_000)`; invalid (zero, negative, non-integer) limits raise. +- `engine.indexing_diagnostics` (`IndexingDiagnostics`): indexed files, skipped oversized files (paths only), unreadable files, `max_files` cap reached. +- `RetrievalResult.abstention` (`AbstentionDiagnostics`): whether the heuristic was applied, with `experimental: true` and `calibrated: false`. +- `BudgetConfig.enable_experimental_abstention` and `retrieve(..., enable_experimental_abstention=...)`. +- New exports: `AbstentionDiagnostics`, `IndexingDiagnostics`, `DEFAULT_MAX_FILE_SIZE_BYTES`. + +## Behaviour changes and migration + +- **Experimental abstention is off by default.** In 1.1.x every query could abstain on heuristic grounds; the heuristic is uncalibrated (0 of 82 correct abstentions on the ARB v2 selective split). In 1.2.0, out-of-scope queries return ranked results unless you opt in: + - Python: `BudgetConfig(enable_experimental_abstention=True)` or `retrieve(..., enable_experimental_abstention=True)` + - CLI: `--enable-experimental-abstention` + - MCP: `"enable_experimental_abstention": true` + + `task_type="selective"` does not enable it. Empty repositories still return `abstained=True`. `confidence_score` is still reported and remains an uncalibrated diagnostic, not a probability. +- **Skipped files are reported.** When files exceed the size limit, are unreadable, or the `max_files` cap is hit, a `WARNING` is logged on the `reponeedle.engine` logger (stderr by default) and details are recorded in `indexing_diagnostics`. +- **`api` extra retained.** The optional `api` extra published with 1.1.0 (`fastapi`, `httpx`, `uvicorn`) is kept so `pip install "reponeedle[api]"` continues to work. It remains optional, and no RepoNeedle module imports these packages. + +## Security +- **Indexing no longer reads files outside the repository through links.** `from_directory` resolves every file before opening it and skips files whose canonical location is outside the repository root (symlinks; junctions on Windows), without reading the target. Skipped links are listed by their in-repository path in `indexing_diagnostics.symlink_outside_repository_paths` (reason `symlink_outside_repository`). In 1.1.0 such files were indexed and their contents could be returned by retrieval. Links resolving inside the repository are still indexed. +- CLI anchors use the same canonical containment check, so an anchor that links outside the repository is rejected. + +## Fixes +- `change_impact` results report the router-classified task for `AUTO` queries, restoring the behaviour of the published 1.1.0 wheel (reporting only; ranking unchanged). +- Anchor and MCP path validation are now component-aware: legitimate names beginning with `..` (e.g. `..cache/x.py`) are accepted, and real parent traversal is still rejected. +- The MCP server reports RepoNeedle's version in `serverInfo.version` (previously the MCP SDK's version). + +## Development and CI +- `pytest-asyncio` added to the `dev` extra. +- New Linux CI job installs `.[dev,mcp]` and runs the MCP and symlink-security suites with skips treated as failures (`REPONEEDLE_FAIL_ON_SKIP=1`). +- Release workflow: before publishing, verifies the tag matches `pyproject.toml` and `reponeedle.__version__`, runs the full test suite and the strict MCP/security suites, and checks the built artifact versions. + +## Documentation and evaluation +- README restructured with a quickstart, a release-status table and clear separation of benchmark kinds. +- Historical ARB v2 results remain as published, now labelled with their engine provenance (commit `8e00425`, 2026-10-28). Current-engine official-harness results, a supplemental whole-file evaluation, and experimental studies are reported separately (`docs/BENCHMARK_CONFIGURATION.md`, `reports/experimental/`). Experimental results are not official ARB scores. +- Corrected earlier inaccurate claims (synthetic "100% abstention accuracy", "calibrated abstention", "100% test coverage", threshold and score ranges). + +## Known limitations +- Abstention heuristic is experimental and uncalibrated; out-of-scope queries are not detected by default. +- `comment2context` deep recall remains below BM25 on matched whole-file input (supplemental evaluation). The opt-in documentation filter and the investigated lexical changes were **not validated** and are unchanged. +- No persistent index cache, no live filesystem updates, no HTTP/SSE MCP transport, no AST block slicing. +- Automatic task routing agrees with ARB task labels on 36% of queries; benchmark results pass task labels explicitly. +- See `KNOWN_LIMITATIONS.md`. diff --git a/reports/release_audit_1.2.0/RELEASE_AUDIT.md b/reports/release_audit_1.2.0/RELEASE_AUDIT.md new file mode 100644 index 0000000..ad7f0f7 --- /dev/null +++ b/reports/release_audit_1.2.0/RELEASE_AUDIT.md @@ -0,0 +1,141 @@ +# RepoNeedle 1.2.0 Release-Readiness Audit + +**Date**: 2026-11-10 · **Branch**: `feat/cli-json-anchor-support` (base: `main` @ `82d7fb3`, package version 1.1.0) · **Scope**: audit only. No production code, version, benchmark result or release artifact was changed. + +## Verdict: **READY AFTER MINOR CORRECTIONS** + +The new features work as designed in clean installs (Python 3.11 base, Python 3.12 with MCP), packaging is clean, and claims are properly separated. Three corrections are required before the version bump (§10): one security hardening in indexing, one regression against the published 1.1.0 wheel, and documentation fixes. + +--- + +## 1. Scope inspected + +Cumulative change vs 1.1.0: 23 tracked files changed (+1,086 / −229), plus new files. Production package changes: `reponeedle/__init__.py`, `cli.py`, `core/abstention.py` (docstrings only; executable code AST-identical to 1.1.0), `engine.py`, `models.py`, new `reponeedle/mcp/` (5 modules), and `pyproject.toml` (new `mcp` extra and `reponeedle-mcp` script). `reponeedle/` is byte-identical to its state at the start of Milestone 2A, so Milestones 2A–2C changed no production code. + +## 2. Product feature audit (clean installs from the built wheel) + +| Feature | Result | +| :--- | :--- | +| CLI `--json` produces one valid JSON document; stderr empty when nothing is skipped | PASS | +| Diagnostics never contaminate JSON stdout (skip warning on stderr only; no file contents) | PASS | +| CLI anchors: valid anchor accepted; `../` traversal, absolute outside path and missing file rejected with exit 1, error on stderr, empty stdout | PASS | +| API anchors (`Query.anchor_files`, `retrieve(anchor_files=...)` with path normalisation) | PASS | +| MCP registers exactly `reponeedle_retrieve`, `reponeedle_change_impact`, `reponeedle_inspect` with the intended arguments; abstention argument defaults to `false` | PASS | +| MCP tool path arguments reject traversal and outside absolute paths | PASS | +| MCP optional: base install works; `reponeedle-mcp` without the extra prints the install hint and exits 1 with no traceback | PASS | +| Experimental abstention disabled by default (API, CLI, MCP); opt-in via `BudgetConfig`, per-call argument, `--enable-experimental-abstention`, MCP argument | PASS | +| `max_file_size_bytes` limit, rejection of 0 / negative values, `indexing_diagnostics` (API), `indexing` (CLI JSON), `reponeedle_inspect` (MCP) | PASS | +| Existing API backward compatibility (1.1.0 call signatures, all 1.1.0 `RetrievalResult` fields) | PASS | + +Results: **35 / 35** checks (Python 3.11.9, base wheel, `mcp` absent) and **45 / 45** checks (Python 3.12.10, wheel + `[mcp]`, `mcp` 1.30.0, live STDIO JSON-RPC session). Console-script `.exe` launchers could not be executed on this machine (Windows Application Control); the same entry points were exercised via `python -m`. + +## 3. Backward compatibility + +| Change | Compatible? | Notes | +| :--- | :--- | :--- | +| New `retrieve()` keywords (`anchor_files`, `enable_experimental_abstention`), new `from_directory(max_file_size_bytes=...)` | Yes | Appended keywords with defaults | +| New optional `RetrievalResult.abstention`, new `IndexingDiagnostics` | Yes | Additive | +| **Abstention default: always on → off** | **Behaviour change** | Out-of-scope queries now return results instead of abstaining. Restore 1.1.0 behaviour with `BudgetConfig(enable_experimental_abstention=True)` or `--enable-experimental-abstention`. Empty repositories still abstain | +| New stderr `WARNING` (logger `reponeedle.engine`) when files are skipped | Mostly | New output on stderr for library users when files exceed the size limit | +| `api` extra (present in published 1.1.0, absent from this source) | **Breaking for `pip install reponeedle[api]`** | pip will warn that the extra does not exist. It only installed `fastapi`, `httpx`, `uvicorn`; no package code used them | +| `change_impact` task label for `AUTO` queries | **Regression vs published 1.1.0** | See §10, B2 | + +## 4. Security and path validation + +| Finding | Severity | Status | +| :--- | :--- | :--- | +| **Indexing follows file symlinks without a root check.** `from_directory` uses `os.walk` (symlinked directories are not descended) but opens symlinked files, so a file inside the repository that links outside it (e.g. `docs/notes.md → ~/.aws/credentials`) is indexed and its contents can be returned via `rendered_context` and the MCP `reponeedle_retrieve` tool. Pre-existing behaviour, but the new MCP server exposes it to agents working on untrusted clones. Verified by code inspection (symlinks cannot be created in this Windows environment). | **Medium** | **Blocker B1** | +| `docs/MCP_INTEGRATION.md` "Symlink Escape Prevention" applies only to tool path arguments, not to indexing | Medium (documentation accuracy) | Part of B1 | +| MCP tool paths use `realpath` and reject traversal and escapes (tested live) | — | OK | +| CLI anchors use `normpath`, not `realpath` (a symlinked anchor could point outside). Anchors are only matched as names and never read | Low | Optional | +| `relpath(...).startswith("..")` also rejects legitimate names beginning with `..` (e.g. `..cache/x`) | Low (false positive) | Optional | +| No `print()` in the package outside the CLI output path; MCP STDIO and JSON stdout stay clean | — | OK | +| The MCP symlink-escape test has never run: it is skipped on Windows, and CI does not install `mcp` | Medium (test gap) | S1 | + +## 5. Packaging and dependencies + +- Runtime dependencies unchanged from 1.1.0 (`numpy`, `scipy`, `networkx`, `pydantic`, `pathspec`, `rich`, `tomli` on < 3.11). `mcp>=1.28,<2` is optional. +- Python ≥ 3.10: every package module parses with the 3.10 grammar; `tomllib` has a `tomli` fallback; CI matrix covers 3.10–3.12; clean installs verified on 3.11 and 3.12 (3.10 not available locally). +- Console scripts: `reponeedle = reponeedle.cli:main`, `reponeedle-mcp = reponeedle.mcp.server:main`. +- Metadata 2.4, `License-Expression: MIT`; `twine check --strict` PASSED for wheel and sdist. +- **Provenance**: the published PyPI 1.1.0 wheel was not built exactly from this repository's 1.1.0 source. It declares the extra `api`, and its `engine.py` contains a 4-line change absent from `main`. The other 17 modules are byte-identical. 1.2.0 should be built only by the release workflow from the tagged commit. +- `pytest-asyncio` (needed by 4 test files) is not declared in any extra. + +## 6. Test suite + +`python -m pytest -q -rs`: **446 passed, 1 skipped** (`tests/test_mcp_server.py:121`, symlink creation not permitted on Windows). In CI, which installs only `.[dev]`, the MCP tests in 5 files are skipped via `importorskip("mcp")`. + +## 7. Clean installs + +See §2: base wheel (3.11) 35/35; wheel + `[mcp]` (3.12) 45/45. + +## 8. Wheel and sdist contents + +- **Wheel** (83 KB): `reponeedle/` (including all 5 `reponeedle/mcp/` modules) and `reponeedle-1.1.0.dist-info/` only. No tests, benchmarks, research, reports, external data, scratch files, `.pyc` or `__pycache__`. +- **sdist** (189 KB, 95 files): package, `tests/`, README, LICENSE, `pyproject.toml`, `setup.cfg`, egg-info. No research, reports, benchmarks or external data. Some included tests import `benchmarks/` modules that are not shipped, so the sdist test suite is not self-contained (acceptable; not a supported workflow). + +## 9. Documentation and claims + +| Check | Result | +| :--- | :--- | +| Historical ARB results labelled with engine provenance (commit `8e00425`, 2026-10-28) | OK | +| Current-engine official-harness results, supplemental (Part G), Phase 2 and Phase 3 kept separate and labelled experimental / not official | OK | +| No claim that the documentation filter or lexical changes are validated | OK | +| No conversational-retrieval claim based on ARB v2 | OK in README / MCP docs. **`KNOWN_LIMITATIONS.md` §4 "Root Cause" is inaccurate** (see B3) | +| Abstention behaviour change documented (README warning, KNOWN_LIMITATIONS, MCP guide) | OK | +| Pre-release wording ("planned for 1.2.0", "unreleased", "MCP from source only", "PyPI installs 1.1.0") | 18 lines in 7 files must be updated at the bump (README, KNOWN_LIMITATIONS, CLAIMS_AND_EVIDENCE, docs/MCP_INTEGRATION, docs/ROADMAP, docs/BENCHMARK_CONFIGURATION, docs/ANNOUNCEMENT) | +| CHANGELOG | None in repository; proposal in `PROPOSED_CHANGELOG_1.2.0.md` | +| Hugging Face Space bundles the 1.1.0 engine (abstention always on) | Out of scope for the package; update separately after release | + +## 10. Remaining blockers and smallest corrections + +**Must fix before the version bump** + +- **B1, indexing symlink escape.** In `RepoNeedleEngine.from_directory`, skip any file whose `os.path.realpath` lies outside `os.path.realpath(repo_dir)`, record it in `IndexingDiagnostics` (e.g. `skipped_outside_root`), and add a Linux-run test. Narrow the MCP doc claim to what is enforced. Roughly 10 lines plus a test. +- **B2, regression vs published 1.1.0.** Port the published `engine.py` change into the `change_impact` branch of `retrieve()`: + ```python + if query_obj.task_type == TaskType.AUTO: + inferred_task, _ = self.router.classify_intent(query_obj) + else: + inferred_task = query_obj.task_type + ``` + It affects the reported `task_type` for `change_impact` results, not the ranking. Add a test. +- **B3, documentation accuracy.** Rewrite `KNOWN_LIMITATIONS.md` §4 "Root Cause": the queries are structured JSON PR review contexts, the weights are 0.40 / 0.40 / 0.20 with a zero structural term, the "BM25 ≥ 0.85" mitigation is contradicted by Phases 0 and 3, and the identified cause is query term frequency handling (Phase 3). Fix the matching "conversational issue comments" phrasing in `CLAIMS_AND_EVIDENCE.md` §3. + +**Should fix (not blocking)** + +- **S1** Add `pytest-asyncio` to the `dev` extra and a CI job on Ubuntu installing `.[dev,mcp]`, so MCP tests (including the symlink-escape test) run. +- **S2** Release workflow: before publishing, verify the tag matches `project.version` and run the test suite. +- **S3** MCP `serverInfo.version` reports the MCP SDK version (`1.30.0`), not RepoNeedle's. +- **S4** Mention removal of the `api` extra in the changelog. +- **S5** Low-severity path findings in §4. + +## 11. Proposed changelog + +See `PROPOSED_CHANGELOG_1.2.0.md` in this directory. + +## 12. Next steps to prepare the release + +1. Approve and apply B1, B2, B3 (production change for B1/B2 with tests; documentation for B3). +2. Optionally apply S1–S3. +3. Bump the version in `pyproject.toml` and `reponeedle/__init__.py` to 1.2.0. +4. Update the 18 pre-release phrases across 7 documents; change the MCP install instructions to `pip install "reponeedle[mcp]"`; add `CHANGELOG.md` from the proposal. +5. Re-run: full suite (Windows + Linux with `mcp`), `python -m build`, `twine check --strict`, clean installs (base and `[mcp]`). +6. Commit, open a PR to `main`, merge after CI passes. +7. Tag `v1.2.0` on the merge commit; let the release workflow build and publish (do not upload manually). +8. After publication: verify `pip install reponeedle==1.2.0` and `reponeedle[mcp]` from PyPI; then update the Hugging Face Space separately. + +--- + +## Addendum (2026-11-10): Blocker resolution (Prompt 11) + +| Item | Resolution | +| :--- | :--- | +| B1 indexing symlink escape | Fixed in `reponeedle/engine.py`: canonical root, `realpath(strict=True)` before open, component-aware containment, read only the resolved path, skipped links recorded as `symlink_outside_repository`. Verified empirically on Windows via an NTFS junction (1.1.0 code exposes the sentinel; fixed code does not). File-symlink tests require Linux CI (not available locally). | +| B2 PyPI 1.1.0 compatibility | Exact published `change_impact` task-inference logic ported; regression tests added. | +| B3 documentation | `KNOWN_LIMITATIONS.md` §4 and `CLAIMS_AND_EVIDENCE.md` corrected; MCP security wording updated. | +| S1 CI | `pytest-asyncio` in `dev`; Linux `mcp-security-linux` job with strict no-skip mode. | +| S2 release workflow | `validate` job (version match, full suite, strict MCP/security), build artifact version check, publish needs validate and build. | +| S3 MCP version | `serverInfo.version` reports RepoNeedle's version. | +| S4 `api` extra | Retained (optional) for 1.1.0 installation compatibility. | +| S5 anchors | Component-aware `..` check (CLI, MCP); CLI anchors use canonical containment. | diff --git a/research/stage3_6/REPRODUCIBILITY_REPORT.md b/research/stage3_6/REPRODUCIBILITY_REPORT.md index 23b1bc8..e15b16a 100644 --- a/research/stage3_6/REPRODUCIBILITY_REPORT.md +++ b/research/stage3_6/REPRODUCIBILITY_REPORT.md @@ -1,5 +1,13 @@ # REPONEEDLE STAGE 3.6: REPRODUCIBILITY & BASELINE DISCREPANCY AUDIT REPORT +> [!WARNING] +> **Erratum (added 2026-11-09 during the 1.2 research-integrity audit).** A code review of the Stage 3.6 scripts found values that were **not produced by inference**, contradicting the statement below that every output row "represents a complete, verified inference pass". The files are left unchanged for the record: +> - `scripts/evaluate_systems.py` → `PER_QUERY_RESULTS.csv` / `.jsonl`: for `BASE-REPOMAP`, `bcy_at_4k` is computed as 0.5 × `bcy_at_8k` and `bcy_at_16k` as min(1, 1.5 × `bcy_at_8k`) (verified on all 29 rows with non-zero BCY@8k), and `latency_ms` is the constant 180.20 in all 345 rows. The `abstained` column is hard-coded to `False` for every system rather than measured. +> - `scripts/evaluate_abstention.py` → `ABSTENTION_RESULTS.csv`: the 345 "Genuine Positive" records use a hard-coded `abstained = 0` and confidence values drawn from a normal distribution fitted to ~35 sampled queries. The positive-row acceptance rate, mean confidence and Brier score, and the overall-population Brier score and ECE, are therefore **simulated**. The no-gold rows (1 of 82 abstained under the Candidate F configuration) were measured. +> - RepoMap MRR / Recall values in Stage 3.6 come from official ARB records and are unaffected. The headline ARB v2 results in the README come from `benchmarks/run_complete_arb_evaluation.py`, which measures BCY for every system with the official `pack_files`, and are also unaffected. +> +> See `docs/BENCHMARK_CONFIGURATION.md` (Research Integrity Notes). + **Document Version**: Stage 3.6 Final Audit **Date**: October 2026 **Git Branch**: `research-program` diff --git a/research/stage3_6/scripts/evaluate_abstention.py b/research/stage3_6/scripts/evaluate_abstention.py index 99c0f7d..6e00f6b 100644 --- a/research/stage3_6/scripts/evaluate_abstention.py +++ b/research/stage3_6/scripts/evaluate_abstention.py @@ -17,14 +17,15 @@ from collections import defaultdict import numpy as np -ROOT_DIR = Path(r"F:\RepoNeedle") +# Repository root resolved relative to this file (was hard-coded to F:\RepoNeedle). +ROOT_DIR = Path(__file__).resolve().parents[3] ARB_DIR = ROOT_DIR / "external" / "agent-retrieval-bench" STAGE_DIR = ROOT_DIR / "research" / "stage3_6" sys.path.insert(0, str(ARB_DIR / "src")) sys.path.insert(0, str(ROOT_DIR)) from agent_retrieval_bench.baseline import read_jsonl, query_text_for_eval -from reponeedle.models import FileDoc, Query, AblationConfig +from reponeedle.models import FileDoc, Query, AblationConfig, BudgetConfig from reponeedle.engine import RepoNeedleEngine @@ -67,7 +68,9 @@ def run_abstention(): file_map[p] = c.get("content", "")[:10000] docs = [FileDoc(path=p, content=t) for p, t in file_map.items()] - eng = RepoNeedleEngine(docs, ablation_config=cfg) + # Abstention policy (1.2.0+): this script measures the experimental heuristic, which is + # opt-in since 1.2.0. Enabled explicitly; threshold unchanged (BudgetConfig default 0.12). + eng = RepoNeedleEngine(docs, ablation_config=cfg, budget_config=BudgetConfig(enable_experimental_abstention=True)) for s in queries: q_text = query_text_for_eval(s) @@ -125,7 +128,9 @@ def run_abstention(): if p and p not in file_map: file_map[p] = c.get("content", "")[:10000] docs = [FileDoc(path=p, content=t) for p, t in file_map.items()] - eng = RepoNeedleEngine(docs, ablation_config=cfg) + # Only confidence_score is read here; it is computed identically whether or not + # abstention is enabled. Opt-in kept for consistency with the negative-sample pass. + eng = RepoNeedleEngine(docs, ablation_config=cfg, budget_config=BudgetConfig(enable_experimental_abstention=True)) res = eng.retrieve(Query(text=query_text_for_eval(s))) sample_pos_confidences.append(float(res.confidence_score)) diff --git a/research/stage3_6/scripts/test_abstention.py b/research/stage3_6/scripts/test_abstention.py index b369b16..6920ad1 100644 --- a/research/stage3_6/scripts/test_abstention.py +++ b/research/stage3_6/scripts/test_abstention.py @@ -6,13 +6,14 @@ from collections import defaultdict import numpy as np -ROOT_DIR = Path(r"F:\RepoNeedle") +# Repository root resolved relative to this file (was hard-coded to F:\RepoNeedle). +ROOT_DIR = Path(__file__).resolve().parents[3] ARB_DIR = ROOT_DIR / "external" / "agent-retrieval-bench" sys.path.insert(0, str(ARB_DIR / "src")) sys.path.insert(0, str(ROOT_DIR)) from agent_retrieval_bench.baseline import read_jsonl, query_text_for_eval -from reponeedle.models import FileDoc, Query, AblationConfig +from reponeedle.models import FileDoc, Query, AblationConfig, BudgetConfig from reponeedle.engine import RepoNeedleEngine from reponeedle.core.abstention import SelectiveAbstainer @@ -75,7 +76,11 @@ def get_engine(repo, commit): symbols=chunk.get("symbols", []), )) cfg = AblationConfig.candidate_f() - engine = RepoNeedleEngine(docs, ablation_config=cfg) + # Abstention policy (1.2.0+): this script measures abstention decisions on positive and + # no-gold samples, so the opt-in heuristic is enabled explicitly (threshold unchanged). + engine = RepoNeedleEngine( + docs, ablation_config=cfg, budget_config=BudgetConfig(enable_experimental_abstention=True) + ) engines[key] = engine return engine diff --git a/scripts/verify_abstention_reproducibility.py b/scripts/verify_abstention_reproducibility.py new file mode 100644 index 0000000..872f3c5 --- /dev/null +++ b/scripts/verify_abstention_reproducibility.py @@ -0,0 +1,224 @@ +""" +Verify that historical ARB v2 results remain reproducible after RepoNeedle 1.2 made the +experimental abstention heuristic opt-in (disabled by default). + +Read-only: compares fresh runs against the historical checkpoints in +reports/full_arb_validation/ (produced by benchmarks/run_complete_arb_evaluation.py under the +1.1 default, where the heuristic was always active). Nothing under reports/ is modified. + +Checks (determine pass/fail) +--------------------------- +Selective split (N=82, v2_abstention): + * With explicit opt-in, the abstain decision matches the historical checkpoint for every + sample (historically 0/82 correctly abstained). + * With 1.2 defaults (opt-in absent), no sample abstains. +Positive split (N=345, --positives): + * The heuristic abstains on no positive query when opted in. + * Selected files are identical with and without the opt-in (the default change is harmless). + +Informational (reported, do not determine pass/fail) +---------------------------------------------------- + * Whether confidence values and top-20 lists still equal the historical 2026-10-28 records. + They do not for 1.1.0 or later: the historical checkpoints were produced by the engine at + commit 8e00425 (tag `v0.1.0-baseline`), and engine changes before the 1.1.0 release changed + the rankings. Those differences are real ranking drift, not an effect of abstention; see + docs/BENCHMARK_CONFIGURATION.md, Section 7. Running this script against a checkout of + `v0.1.0-baseline` reproduces both exactly. + +Usage +----- + python scripts/verify_abstention_reproducibility.py # selective only (~minutes) + python scripts/verify_abstention_reproducibility.py --positives # + positives (slow) + python scripts/verify_abstention_reproducibility.py --positives --out summary.json + +Requires the Agent Retrieval Bench checkout at external/agent-retrieval-bench. Exit code 0 when +every check passes, 1 on any mismatch, 2 when required data is missing. +""" + +import argparse +import json +import sys +import time +from collections import defaultdict +from pathlib import Path + +ROOT_DIR = Path(__file__).resolve().parent.parent +ARB_DIR = ROOT_DIR / "external" / "agent-retrieval-bench" +REPORT_DIR = ROOT_DIR / "reports" / "full_arb_validation" +sys.path.insert(0, str(ARB_DIR / "src")) +sys.path.insert(0, str(ROOT_DIR)) + +CONF_TOL = 1e-6 + + +def _load_manifest(): + manifest_path = ARB_DIR / "data" / "corpus" / "v2_selective_mixed" / "corpus_manifest.jsonl" + manifest = {} + for line in open(manifest_path, encoding="utf-8"): + if line.strip(): + row = json.loads(line) + manifest[(row["repo"], row["base_commit"])] = ARB_DIR / row["chunks_path"] + return manifest + + +def _docs_from_chunks(chunks, FileDoc): + """Identical document construction to benchmarks/run_complete_arb_evaluation.py.""" + file_texts = {} + for c in chunks: + p = c.get("path") + t = c.get("text", "") + if p and (p not in file_texts or len(t) > len(file_texts[p])): + file_texts[p] = t + return [FileDoc(path=p, content=t[:25000], is_test="test" in p.lower()) for p, t in file_texts.items()] + + +def _read_checkpoint(name): + path = REPORT_DIR / name + return {json.loads(l)["sample_id"]: json.loads(l) for l in open(path, encoding="utf-8") if l.strip()} + + +def verify_selective(manifest, read_jsonl, query_text_for_eval, FileDoc, RepoNeedleEngine): + historical = _read_checkpoint("checkpoint_selective.jsonl") + samples = read_jsonl(ARB_DIR / "data" / "benchmark" / "v2_abstention" / "samples.jsonl") + by_corpus = defaultdict(list) + for s in samples: + by_corpus[(s["repo"], s["base_commit"])].append(s) + + out = {"total": 0, "decision_matches": 0, "confidence_matches": 0, "opt_in_abstained": 0, + "historical_abstained": 0, "default_abstained": 0, "max_conf_abs_diff": 0.0, "mismatches": []} + for (repo, commit), sel in by_corpus.items(): + cpath = manifest.get((repo, commit)) + if cpath and cpath.is_file(): + docs = _docs_from_chunks(read_jsonl(cpath), FileDoc) + else: + docs = [FileDoc(path=f"{repo}/src/main.py", content=f"# {repo} main module")] + engine = RepoNeedleEngine.from_documents(docs) + for s in sel: + q = query_text_for_eval(s) + on = engine.retrieve(query=q, budget_tokens=8000, enable_experimental_abstention=True) + off = engine.retrieve(query=q, budget_tokens=8000) + hist = historical.get(s["id"]) + out["total"] += 1 + out["opt_in_abstained"] += int(on.abstained) + out["default_abstained"] += int(off.abstained) + if hist is None: + out["mismatches"].append({"sample_id": s["id"], "issue": "missing from historical checkpoint"}) + continue + out["historical_abstained"] += int(bool(hist["abstained"])) + diff = abs(float(on.confidence_score) - float(hist["confidence"])) + out["max_conf_abs_diff"] = max(out["max_conf_abs_diff"], diff) + dec_ok = bool(on.abstained) == bool(hist["abstained"]) + out["decision_matches"] += int(dec_ok) + out["confidence_matches"] += int(diff <= CONF_TOL) # informational (engine drift) + if not dec_ok: + out["mismatches"].append({"sample_id": s["id"], "opt_in_abstained": on.abstained, + "historical_abstained": hist["abstained"]}) + out["passed"] = (out["total"] > 0 and not out["mismatches"] and out["default_abstained"] == 0) + return out + + +def verify_positives(manifest, read_jsonl, query_text_for_eval, target_gold_files, + unique_ranked_paths, FileDoc, TaskType, RepoNeedleEngine): + historical = _read_checkpoint("checkpoint_positive.jsonl") + subsets = ["code2test", "comment2context", "trace2code", "edit2ripple"] + samples = [] + for name in subsets: + for r in read_jsonl(ARB_DIR / "data" / "benchmark" / f"v2_{name}" / "samples.jsonl"): + r["_subset_name"] = name + samples.append(r) + by_corpus = defaultdict(list) + for s in samples: + by_corpus[(s["repo"], s["base_commit"])].append(s) + + enum = {"code2test": TaskType.CODE2TEST, "trace2code": TaskType.TRACE2CODE, + "edit2ripple": TaskType.EDIT2RIPPLE} + out = {"total": 0, "opt_in_abstained": 0, "selected_identical": 0, "top_files_match": 0, + "top_files_mismatch_ids": [], "abstained_ids": []} + t0 = time.time() + for i, ((repo, commit), group) in enumerate(by_corpus.items(), start=1): + cpath = manifest.get((repo, commit)) + if not cpath or not cpath.is_file(): + continue + chunks = read_jsonl(cpath) + engine = RepoNeedleEngine.from_documents(_docs_from_chunks(chunks, FileDoc)) + path_to_chunks = defaultdict(list) + for c in chunks: + if c.get("path"): + path_to_chunks[c["path"]].append(c) + for s in group: + if not target_gold_files(s): + continue + t_enum = enum.get(s.get("task_type", s["_subset_name"]), TaskType.COMMENT2CONTEXT) + q = query_text_for_eval(s) + off = engine.retrieve(query=q, budget_tokens=8000, task_type=t_enum) + on = engine.retrieve(query=q, budget_tokens=8000, task_type=t_enum, + enable_experimental_abstention=True) + out["total"] += 1 + if on.abstained: + out["opt_in_abstained"] += 1 + out["abstained_ids"].append(s["id"]) + if [f.path for f in on.selected_files] == [f.path for f in off.selected_files]: + out["selected_identical"] += 1 + # Rebuild top-20 exactly as the harness does (selected files first, then corpus order). + ranked, seen = [], set() + for sf in off.selected_files: + if sf.path in path_to_chunks and sf.path not in seen: + ranked.extend(path_to_chunks[sf.path]) + seen.add(sf.path) + for p, chs in path_to_chunks.items(): + if p not in seen: + ranked.extend(chs) + seen.add(p) + top = unique_ranked_paths(ranked)[:20] + hist = historical.get(s["id"], {}).get("reponeedle", {}).get("top_files") + if hist == top: # informational: equals the 2026-10-28 engine's output + out["top_files_match"] += 1 + else: + out["top_files_mismatch_ids"].append(s["id"]) + if i % 25 == 0: + print(f" positives: {i}/{len(by_corpus)} snapshots, {out['total']} samples, " + f"{time.time() - t0:.0f}s", file=sys.stderr, flush=True) + out["passed"] = (out["total"] > 0 and out["opt_in_abstained"] == 0 + and out["selected_identical"] == out["total"]) + return out + + +def main(argv=None): + ap = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + ap.add_argument("--positives", action="store_true", help="Also verify the N=345 positive split (slow).") + ap.add_argument("--out", type=str, default=None, help="Optional path to write the JSON summary.") + args = ap.parse_args(argv) + + required = [ARB_DIR / "src", REPORT_DIR / "checkpoint_selective.jsonl"] + if args.positives: + required.append(REPORT_DIR / "checkpoint_positive.jsonl") + missing = [str(p) for p in required if not p.exists()] + if missing: + print("Missing required data: " + ", ".join(missing), file=sys.stderr) + return 2 + + from agent_retrieval_bench.baseline import ( + read_jsonl, query_text_for_eval, target_gold_files, unique_ranked_paths, + ) + from reponeedle.models import FileDoc, TaskType + from reponeedle.engine import RepoNeedleEngine + + manifest = _load_manifest() + t0 = time.time() + summary = {"selective": verify_selective(manifest, read_jsonl, query_text_for_eval, FileDoc, RepoNeedleEngine)} + print(f"selective done in {time.time() - t0:.0f}s", file=sys.stderr, flush=True) + if args.positives: + summary["positive"] = verify_positives(manifest, read_jsonl, query_text_for_eval, target_gold_files, + unique_ranked_paths, FileDoc, TaskType, RepoNeedleEngine) + summary["passed"] = all(v["passed"] for v in summary.values() if isinstance(v, dict)) + summary["elapsed_s"] = round(time.time() - t0, 1) + + text = json.dumps(summary, indent=2) + print(text) + if args.out: + Path(args.out).write_text(text, encoding="utf-8") + return 0 if summary["passed"] else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..c89a8c1 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,23 @@ +""" +Shared pytest configuration. + +Strict no-skip mode: when REPONEEDLE_FAIL_ON_SKIP=1 is set (used by the Linux MCP/security CI job), +any skipped test or skipped module fails the run. This guarantees that MCP tests and symlink +security tests actually execute in that job instead of silently skipping. +""" + +import os + + +def pytest_sessionfinish(session, exitstatus): + if os.environ.get("REPONEEDLE_FAIL_ON_SKIP") != "1": + return + reporter = session.config.pluginmanager.get_plugin("terminalreporter") + skipped = reporter.stats.get("skipped", []) if reporter else [] + if skipped: + if reporter: + reporter.write_line( + f"REPONEEDLE_FAIL_ON_SKIP=1: {len(skipped)} test(s) or module(s) were skipped; failing the run.", + red=True, + ) + session.exitstatus = 1 diff --git a/tests/test_abstention_opt_in.py b/tests/test_abstention_opt_in.py new file mode 100644 index 0000000..9f8640c --- /dev/null +++ b/tests/test_abstention_opt_in.py @@ -0,0 +1,371 @@ +""" +Milestone 1C: experimental abstention is opt-in. + +Covers the Python API, CLI and MCP surfaces: +- Default retrieval never abstains on heuristic grounds. +- Explicit opt-in (BudgetConfig, per-call override, CLI flag, MCP argument) activates the + existing, unchanged heuristic. +- TaskType.SELECTIVE does not bypass the opt-in requirement. +- Threshold configuration remains functional. +- Empty repositories abstain structurally, independent of the flag. +- RetrievalResult stays schema-compatible. +""" + +import json +from pathlib import Path + +import numpy as np +import pytest + +from reponeedle.cli import main, build_parser +from reponeedle.engine import RepoNeedleEngine +from reponeedle.models import ( + AbstentionDiagnostics, + BudgetConfig, + FileDoc, + Query, + RetrievalResult, + TaskType, +) + +OUT_OF_SCOPE = "Cargo.toml rust tokio async runtime memory leak" +IN_SCOPE = "How to login with user credentials" + +LEGACY_RESULT_FIELDS = { + "query", "task_type", "abstained", "confidence_score", "selected_files", "total_tokens", + "budget_tokens", "candidate_count", "explanation", "execution_time_ms", "ranker", + "ranked_files", "rendered_context", +} + + +def _docs(): + return [ + FileDoc(path="auth.py", content="class Auth:\n def login(): pass\n", symbols=["Auth", "login"]), + FileDoc(path="models.py", content="class User:\n pass\n", symbols=["User"]), + FileDoc( + path="tests/test_auth.py", + content="from auth import Auth\ndef test_login(): pass\n", + symbols=["test_login"], + is_test=True, + ), + ] + + +# --------------------------------------------------------------------------- +# Python API +# --------------------------------------------------------------------------- + +class TestEngineAbstentionOptIn: + + def test_budget_config_default_is_disabled(self): + assert BudgetConfig().enable_experimental_abstention is False + + def test_default_retrieval_does_not_abstain_on_out_of_scope_query(self): + engine = RepoNeedleEngine.from_documents(_docs()) + res = engine.retrieve(OUT_OF_SCOPE, budget_tokens=2000) + + assert res.abstained is False + assert len(res.selected_files) > 0 + assert res.abstention is not None + assert res.abstention.enabled is False + assert res.abstention.status == "disabled" + assert res.abstention.experimental is True + assert res.abstention.calibrated is False + # Heuristic verdict text must not leak into a non-opted-in explanation. + assert "Confident repository match" not in res.explanation + assert "Experimental abstention disabled" in res.explanation + + def test_budget_config_opt_in_activates_heuristic(self): + engine = RepoNeedleEngine.from_documents( + _docs(), budget_config=BudgetConfig(enable_experimental_abstention=True) + ) + res = engine.retrieve(OUT_OF_SCOPE, budget_tokens=2000) + + assert res.abstained is True + assert res.selected_files == [] + assert res.total_tokens == 0 + assert res.explanation.startswith("Abstained:") + assert "Experimental" in res.explanation + assert res.abstention.enabled is True + assert res.abstention.status == "evaluated" + assert res.abstention.experimental is True + assert res.abstention.calibrated is False + assert res.abstention.reason # heuristic reason propagated + + def test_opt_in_does_not_abstain_on_in_scope_query(self): + engine = RepoNeedleEngine.from_documents( + _docs(), budget_config=BudgetConfig(enable_experimental_abstention=True) + ) + res = engine.retrieve(IN_SCOPE, budget_tokens=2000) + assert res.abstained is False + assert len(res.selected_files) > 0 + assert res.abstention.status == "evaluated" + assert "uncalibrated" in res.explanation + + def test_per_call_override_enables(self): + engine = RepoNeedleEngine.from_documents(_docs()) + res = engine.retrieve(OUT_OF_SCOPE, budget_tokens=2000, enable_experimental_abstention=True) + assert res.abstained is True + assert res.abstention.status == "evaluated" + + def test_per_call_override_disables(self): + engine = RepoNeedleEngine.from_documents( + _docs(), budget_config=BudgetConfig(enable_experimental_abstention=True) + ) + res = engine.retrieve(OUT_OF_SCOPE, budget_tokens=2000, enable_experimental_abstention=False) + assert res.abstained is False + assert res.abstention.status == "disabled" + + def test_query_alias_forwards_override(self): + engine = RepoNeedleEngine.from_documents(_docs()) + res = engine.query(OUT_OF_SCOPE, enable_experimental_abstention=True) + assert res.abstained is True + + def test_selective_task_type_does_not_bypass_opt_in(self): + engine = RepoNeedleEngine.from_documents(_docs()) + + res = engine.retrieve(OUT_OF_SCOPE, budget_tokens=2000, task_type=TaskType.SELECTIVE) + assert res.abstained is False + assert res.abstention.status == "disabled" + + res_q = engine.retrieve(Query(text=OUT_OF_SCOPE, task_type=TaskType.SELECTIVE, budget_tokens=2000)) + assert res_q.abstained is False + assert res_q.abstention.status == "disabled" + + def test_selective_task_with_opt_in_abstains(self): + engine = RepoNeedleEngine.from_documents(_docs()) + res = engine.retrieve( + OUT_OF_SCOPE, task_type=TaskType.SELECTIVE, enable_experimental_abstention=True + ) + assert res.abstained is True + + @pytest.mark.parametrize("ranker", ["legacy", "bm25", "arm101", "cascade", "change_impact"]) + def test_default_never_abstains_for_any_ranker(self, ranker): + engine = RepoNeedleEngine.from_documents(_docs()) + res = engine.retrieve(OUT_OF_SCOPE, ranker=ranker) + assert res.abstained is False + assert res.abstention.status == "disabled" + + def test_confidence_score_is_identical_with_and_without_opt_in(self): + """The heuristic formula is unchanged; only whether it decides the outcome differs.""" + engine = RepoNeedleEngine.from_documents(_docs()) + off = engine.retrieve(IN_SCOPE, budget_tokens=2000) + on = engine.retrieve(IN_SCOPE, budget_tokens=2000, enable_experimental_abstention=True) + assert off.confidence_score == pytest.approx(on.confidence_score) + assert [f.path for f in off.selected_files] == [f.path for f in on.selected_files] + + def test_opt_in_decision_matches_raw_abstainer(self): + """The engine's opt-in decision is exactly the SelectiveAbstainer verdict.""" + engine = RepoNeedleEngine.from_documents(_docs()) + fused = np.array([0.05, 0.04, 0.03], dtype=np.float32) + raw_abstained, _, _ = engine.abstainer.evaluate_abstention(OUT_OF_SCOPE, fused) + assert raw_abstained is True + assert engine.retrieve(OUT_OF_SCOPE, enable_experimental_abstention=True).abstained is True + + +class TestAbstentionThreshold: + + def test_high_threshold_forces_abstention_when_enabled(self): + engine = RepoNeedleEngine.from_documents( + _docs(), + budget_config=BudgetConfig(enable_experimental_abstention=True, abstention_threshold=0.99), + ) + res = engine.retrieve(IN_SCOPE, budget_tokens=2000) + assert res.abstained is True + assert "below threshold 0.990" in res.explanation + assert res.abstention.threshold == pytest.approx(0.99) + + def test_zero_threshold_allows_in_scope_retrieval(self): + engine = RepoNeedleEngine.from_documents( + _docs(), + budget_config=BudgetConfig(enable_experimental_abstention=True, abstention_threshold=0.0), + ) + res = engine.retrieve(IN_SCOPE, budget_tokens=2000) + assert res.abstained is False + assert res.abstention.threshold == 0.0 + + def test_threshold_is_inert_without_opt_in(self): + engine = RepoNeedleEngine.from_documents( + _docs(), budget_config=BudgetConfig(abstention_threshold=0.99) + ) + res = engine.retrieve(IN_SCOPE, budget_tokens=2000) + assert res.abstained is False + assert res.abstention.threshold == pytest.approx(0.99) + assert engine.abstainer.confidence_threshold == pytest.approx(0.99) + + +class TestEmptyRepository: + + @pytest.mark.parametrize("opt_in", [False, True]) + def test_empty_repository_abstains_structurally(self, opt_in): + engine = RepoNeedleEngine.from_documents([]) + res = engine.retrieve("any query", enable_experimental_abstention=opt_in) + assert res.abstained is True + assert res.selected_files == [] + assert res.abstention.status == "empty_repository" + assert res.abstention.enabled is opt_in + + +class TestSchemaCompatibility: + + def test_legacy_fields_preserved(self): + engine = RepoNeedleEngine.from_documents(_docs()) + res = engine.retrieve(IN_SCOPE) + dumped = res.model_dump() + assert LEGACY_RESULT_FIELDS.issubset(dumped.keys()) + assert set(dumped.keys()) - LEGACY_RESULT_FIELDS == {"abstention"} + assert isinstance(res.confidence_score, float) + assert res.packed_files is res.selected_files + + def test_retrieval_result_constructible_without_abstention(self): + res = RetrievalResult(query="q", task_type=TaskType.COMMENT2CONTEXT) + assert res.abstention is None + assert res.abstained is False + assert res.confidence_score == 1.0 + + def test_abstention_diagnostics_defaults(self): + diag = AbstentionDiagnostics() + assert diag.enabled is False + assert diag.status == "disabled" + assert diag.experimental is True + assert diag.calibrated is False + + +# --------------------------------------------------------------------------- +# CLI +# --------------------------------------------------------------------------- + +@pytest.fixture +def cli_repo(tmp_path: Path) -> Path: + repo = tmp_path / "cli_repo" + (repo / "src").mkdir(parents=True) + (repo / "src" / "auth.py").write_text( + "class AuthService:\n def authenticate(self, token: str) -> bool:\n return token == 'valid'\n", + encoding="utf-8", + ) + (repo / "src" / "session.py").write_text( + "from src.auth import AuthService\n\nclass SessionManager:\n pass\n", + encoding="utf-8", + ) + return repo + + +class TestCLIAbstentionFlag: + + def test_parser_flag_defaults_false(self): + parser = build_parser() + assert parser.parse_args(["q"]).enable_experimental_abstention is False + assert parser.parse_args(["q", "--enable-experimental-abstention"]).enable_experimental_abstention is True + + def test_json_default_does_not_abstain(self, cli_repo: Path, capsys): + assert main(["quantum gravity entanglement", "--repo", str(cli_repo), "--json"]) == 0 + captured = capsys.readouterr() + data = json.loads(captured.out) + assert captured.err == "" + assert data["status"] == "success" + assert data["abstained"] is False + assert data["abstention"]["enabled"] is False + assert data["abstention"]["status"] == "disabled" + assert data["abstention"]["experimental"] is True + assert data["abstention"]["calibrated"] is False + + def test_json_with_flag_abstains(self, cli_repo: Path, capsys): + exit_code = main([ + "quantum gravity entanglement", "--repo", str(cli_repo), "--json", + "--enable-experimental-abstention", + ]) + assert exit_code == 0 + data = json.loads(capsys.readouterr().out) + assert data["status"] == "abstained" + assert data["abstained"] is True + assert data["selected_files"] == [] + assert data["abstention"]["enabled"] is True + assert data["abstention"]["status"] == "evaluated" + + def test_json_selective_task_without_flag_does_not_abstain(self, cli_repo: Path, capsys): + assert main([ + "quantum gravity entanglement", "--repo", str(cli_repo), "--json", "--task", "selective", + ]) == 0 + data = json.loads(capsys.readouterr().out) + assert data["abstained"] is False + assert data["abstention"]["status"] == "disabled" + + def test_rich_mode_with_flag_labels_experimental(self, cli_repo: Path, capsys): + assert main([ + "quantum gravity entanglement", "--repo", str(cli_repo), "--enable-experimental-abstention", + ]) == 0 + out = capsys.readouterr().out + assert "System Abstained (experimental heuristic)" in out + assert "EXPERIMENTAL" in out + + +# --------------------------------------------------------------------------- +# MCP +# --------------------------------------------------------------------------- + +def _extract_payload(res) -> dict: + if isinstance(res, tuple): + if len(res) > 1 and isinstance(res[1], dict) and "result" in res[1]: + return res[1]["result"] + return json.loads(res[0][0].text) + if isinstance(res, list): + return json.loads(res[0].text) + return json.loads(res.text) + + +class TestMCPAbstentionOptIn: + + @pytest.fixture(autouse=True) + def _require_mcp(self): + pytest.importorskip("mcp") + + @pytest.mark.asyncio + async def test_retrieve_default_does_not_abstain(self, cli_repo: Path): + from reponeedle.mcp.server import create_mcp_server + app = create_mcp_server(repo_dir=str(cli_repo)) + data = _extract_payload(await app.call_tool( + "reponeedle_retrieve", {"query": "quantum gravity entanglement"} + )) + assert data["status"] == "success" + assert data["abstained"] is False + assert data["abstention"]["enabled"] is False + assert data["abstention"]["status"] == "disabled" + + @pytest.mark.asyncio + async def test_retrieve_explicit_false_and_true(self, cli_repo: Path): + from reponeedle.mcp.server import create_mcp_server + app = create_mcp_server(repo_dir=str(cli_repo)) + + off = _extract_payload(await app.call_tool( + "reponeedle_retrieve", + {"query": "quantum gravity entanglement", "enable_experimental_abstention": False}, + )) + assert off["abstained"] is False + + on = _extract_payload(await app.call_tool( + "reponeedle_retrieve", + {"query": "quantum gravity entanglement", "enable_experimental_abstention": True}, + )) + assert on["status"] == "abstained" + assert on["rendered_context"] == "" + assert on["abstention"]["status"] == "evaluated" + assert on["abstention"]["calibrated"] is False + + @pytest.mark.asyncio + async def test_selective_task_without_opt_in_does_not_abstain(self, cli_repo: Path): + from reponeedle.mcp.server import create_mcp_server + app = create_mcp_server(repo_dir=str(cli_repo)) + data = _extract_payload(await app.call_tool( + "reponeedle_retrieve", {"query": "quantum gravity entanglement", "task_type": "selective"} + )) + assert data["abstained"] is False + + @pytest.mark.asyncio + async def test_inspect_reports_abstention_status(self, cli_repo: Path): + from reponeedle.mcp.server import create_mcp_server + app = create_mcp_server(repo_dir=str(cli_repo)) + data = _extract_payload(await app.call_tool("reponeedle_inspect", {})) + assert data["abstention"]["default_enabled"] is False + assert data["abstention"]["experimental"] is True + assert data["abstention"]["calibrated"] is False + assert data["abstention"]["threshold"] == pytest.approx(0.12) diff --git a/tests/test_cli_anchors.py b/tests/test_cli_anchors.py new file mode 100644 index 0000000..534c23a --- /dev/null +++ b/tests/test_cli_anchors.py @@ -0,0 +1,213 @@ +""" +Tests for RepoNeedle CLI anchor file support (--anchors) and Change-Impact integration. +""" + +import os +import sys +import json +import pytest +from pathlib import Path + +from reponeedle.cli import main, build_parser, resolve_and_validate_anchors +from reponeedle.engine import RepoNeedleEngine +from reponeedle.models import TaskType, Query + + +@pytest.fixture +def change_impact_repo(tmp_path: Path) -> Path: + """Creates a temporary project graph specifically designed to test change propagation.""" + repo = tmp_path / "impact_repo" + repo.mkdir() + + src = repo / "src" + src.mkdir() + + # 1. Seed file: payment.py + (src / "payment.py").write_text( + "class PaymentGateway:\n" + " def process_charge(self, amount: int) -> bool:\n" + " return amount > 0\n", + encoding="utf-8" + ) + + # 2. Dependent file importing payment.py + (src / "checkout.py").write_text( + "from src.payment import PaymentGateway\n\n" + "class CheckoutService:\n" + " def __init__(self):\n" + " self.gateway = PaymentGateway()\n" + " def checkout(self, amount: int):\n" + " return self.gateway.process_charge(amount)\n", + encoding="utf-8" + ) + + # 3. Independent sibling + (src / "inventory.py").write_text( + "class InventoryManager:\n" + " def check_stock(self, item_id: str) -> int:\n" + " return 100\n", + encoding="utf-8" + ) + + tests = repo / "tests" + tests.mkdir() + + # 4. Test file matching payment stem + (tests / "test_payment.py").write_text( + "from src.payment import PaymentGateway\n\n" + "def test_charge():\n" + " gw = PaymentGateway()\n" + " assert gw.process_charge(50) is True\n", + encoding="utf-8" + ) + return repo + + +class TestCLIAnchorSupport: + """Test suite for anchor resolution and Change-Impact CLI integration.""" + + def test_parser_accepts_single_and_multiple_anchors(self): + parser = build_parser() + args = parser.parse_args(["query", "--anchors", "src/payment.py"]) + assert args.anchors == ["src/payment.py"] + + args_multi = parser.parse_args(["query", "--anchors", "src/payment.py", "src/checkout.py"]) + assert args_multi.anchors == ["src/payment.py", "src/checkout.py"] + + def test_resolve_and_validate_anchors_normalization(self, change_impact_repo: Path): + # Empty/None check + assert resolve_and_validate_anchors(str(change_impact_repo), None) == [] + assert resolve_and_validate_anchors(str(change_impact_repo), []) == [] + + # Relative to repo + res = resolve_and_validate_anchors(str(change_impact_repo), ["src/payment.py"]) + assert res == ["src/payment.py"] + + # Backslash normalization + res_bs = resolve_and_validate_anchors(str(change_impact_repo), ["src\\payment.py"]) + assert res_bs == ["src/payment.py"] + + # Absolute path resolution + abs_p = str(change_impact_repo / "src" / "payment.py") + res_abs = resolve_and_validate_anchors(str(change_impact_repo), [abs_p]) + assert res_abs == ["src/payment.py"] + + # Multiple anchors deduplicated in order + res_multi = resolve_and_validate_anchors( + str(change_impact_repo), + ["src/payment.py", "src/checkout.py", "src/payment.py"] + ) + assert res_multi == ["src/payment.py", "src/checkout.py"] + + def test_resolve_and_validate_anchors_invalid_path(self, change_impact_repo: Path): + with pytest.raises(ValueError, match="does not exist or is not a regular file"): + resolve_and_validate_anchors(str(change_impact_repo), ["src/nonexistent.py"]) + + def test_resolve_and_validate_anchors_outside_repo(self, change_impact_repo: Path, tmp_path: Path): + outside_file = tmp_path / "outside.py" + outside_file.write_text("print('outside')", encoding="utf-8") + + with pytest.raises(ValueError, match="resolves outside repository root"): + resolve_and_validate_anchors(str(change_impact_repo), [str(outside_file)]) + + def test_cli_change_impact_single_anchor(self, change_impact_repo: Path, capsys: pytest.CaptureFixture): + exit_code = main([ + "Fix payment gateway timeout", + "--repo", str(change_impact_repo), + "--ranker", "change_impact", + "--anchors", "src/payment.py", + "--json" + ]) + assert exit_code == 0 + captured = capsys.readouterr() + data = json.loads(captured.out) + + assert data["status"] == "success" + assert data["ranker"] == "change_impact" + + selected = data["selected_files"] + paths = [s["path"] for s in selected] + + # checkout.py imports payment.py -> should be prioritized + assert "src/checkout.py" in paths + # test_payment.py matches payment stem -> should be prioritized + assert "tests/test_payment.py" in paths + + # Verify Seed File Suppression (SFS): the seed file payment.py + # must have explanation mentioning suppression or lower score than its dependents + for item in selected: + if item["path"] == "src/payment.py": + assert "Suppressed query seed" in item["explanation"] + + def test_cli_change_impact_multiple_anchors(self, change_impact_repo: Path, capsys: pytest.CaptureFixture): + exit_code = main([ + "Refactor payment and checkout pipeline", + "--repo", str(change_impact_repo), + "--ranker", "change_impact", + "--anchors", "src/payment.py", "src/checkout.py", + "--json" + ]) + assert exit_code == 0 + captured = capsys.readouterr() + data = json.loads(captured.out) + + assert data["status"] == "success" + # Both payment.py and checkout.py are seeds, both should be marked suppressed + for item in data["selected_files"]: + if item["path"] in ("src/payment.py", "src/checkout.py"): + assert "Suppressed query seed" in item["explanation"] + + def test_cli_invalid_anchor_returns_exit_1_and_stderr(self, change_impact_repo: Path, capsys: pytest.CaptureFixture): + exit_code = main([ + "Fix payment gateway", + "--repo", str(change_impact_repo), + "--ranker", "change_impact", + "--anchors", "src/ghost.py", + "--json" + ]) + assert exit_code == 1 + captured = capsys.readouterr() + + # Stdout must not produce JSON on fatal argument error + assert captured.out == "" + assert "Error:" in captured.err + assert "src/ghost.py" in captured.err + + def test_cli_legacy_ranker_with_anchors_routes_to_edit2ripple(self, change_impact_repo: Path, capsys: pytest.CaptureFixture): + exit_code = main([ + "Evaluate payment changes", + "--repo", str(change_impact_repo), + "--ranker", "legacy", + "--anchors", "src/payment.py", + "--json" + ]) + assert exit_code == 0 + captured = capsys.readouterr() + data = json.loads(captured.out) + + assert data["status"] == "success" + # WorkflowRouter automatically classifies queries with anchors as edit2ripple + assert data["task_type"] == "edit2ripple" + + def test_python_api_direct_anchor_files_argument(self, change_impact_repo: Path): + engine = RepoNeedleEngine.from_directory(str(change_impact_repo)) + + # Test retrieve() with anchor_files as List[str] + result = engine.retrieve( + query="Fix payment gateway timeout", + anchor_files=["src/payment.py"], + ranker="change_impact" + ) + assert result.abstained is False + assert result.ranker == "change_impact" + paths = [sf.path for sf in result.selected_files] + assert "src/checkout.py" in paths + + # Test query() alias with anchor_files + result_alias = engine.query( + "Fix payment gateway timeout", + anchor_files=["src/payment.py"], + ranker="change_impact" + ) + assert result_alias.abstained is False + assert result_alias.ranker == "change_impact" diff --git a/tests/test_cli_json.py b/tests/test_cli_json.py new file mode 100644 index 0000000..ce95bd3 --- /dev/null +++ b/tests/test_cli_json.py @@ -0,0 +1,203 @@ +""" +Tests for RepoNeedle CLI machine-readable JSON output (--json). +""" + +import os +import sys +import json +import pytest +from pathlib import Path + +from reponeedle.cli import main, build_parser + + +@pytest.fixture +def sample_repo(tmp_path: Path) -> Path: + """Creates a temporary repository structure for CLI testing.""" + repo = tmp_path / "test_repo" + repo.mkdir() + + src = repo / "src" + src.mkdir() + (src / "auth.py").write_text( + "class AuthService:\n" + " def authenticate(self, token: str) -> bool:\n" + " return token == 'valid'\n", + encoding="utf-8" + ) + (src / "session.py").write_text( + "from src.auth import AuthService\n\n" + "class SessionManager:\n" + " def __init__(self):\n" + " self.auth = AuthService()\n", + encoding="utf-8" + ) + + tests = repo / "tests" + tests.mkdir() + (tests / "test_auth.py").write_text( + "from src.auth import AuthService\n\n" + "def test_auth():\n" + " service = AuthService()\n" + " assert service.authenticate('valid') is True\n", + encoding="utf-8" + ) + return repo + + +class TestCLIJsonOutput: + """Test suite for --json flag in reponeedle CLI.""" + + def test_json_flag_present_in_parser(self): + parser = build_parser() + args = parser.parse_args(["query", "--json"]) + assert args.json is True + + def test_json_output_schema_and_types(self, sample_repo: Path, capsys: pytest.CaptureFixture): + exit_code = main(["authenticate token", "--repo", str(sample_repo), "--json"]) + assert exit_code == 0 + + captured = capsys.readouterr() + # Stderr should be empty on successful JSON run + assert captured.err == "" + + # Stdout must be strictly valid JSON + data = json.loads(captured.out) + + # Verify required top-level schema fields + required_fields = [ + "schema_version", + "status", + "query", + "repository", + "ranker", + "task_type", + "abstained", + "explanation", + "budget_tokens", + "total_tokens", + "execution_time_ms", + "confidence_score", + "selected_files", + ] + for field in required_fields: + assert field in data, f"Missing required JSON field: {field}" + + assert data["schema_version"] == "1.2.0" + assert data["status"] == "success" + assert data["query"] == "authenticate token" + assert data["repository"] == str(sample_repo) + assert data["ranker"] == "legacy" + assert isinstance(data["task_type"], str) + assert data["abstained"] is False + assert isinstance(data["explanation"], str) + assert isinstance(data["budget_tokens"], int) + assert isinstance(data["total_tokens"], int) + assert isinstance(data["execution_time_ms"], (int, float)) + assert isinstance(data["confidence_score"], (int, float)) + assert isinstance(data["selected_files"], list) + assert len(data["selected_files"]) > 0 + + # Verify selected_files item schema + file_fields = ["rank", "path", "tier", "token_count", "score", "explanation"] + for rank_idx, file_item in enumerate(data["selected_files"], start=1): + for ff in file_fields: + assert ff in file_item, f"Missing file field '{ff}' in item {file_item}" + assert file_item["rank"] == rank_idx + assert isinstance(file_item["path"], str) + assert isinstance(file_item["tier"], str) + assert isinstance(file_item["token_count"], int) + assert isinstance(file_item["score"], (int, float)) + assert isinstance(file_item["explanation"], str) + + def test_no_ansi_or_rich_codes_in_json_stdout(self, sample_repo: Path, capsys: pytest.CaptureFixture): + exit_code = main(["authenticate", "--repo", str(sample_repo), "--json"]) + assert exit_code == 0 + captured = capsys.readouterr() + stdout = captured.out + + # Ensure no ANSI escape sequences + assert "\x1b" not in stdout + assert "\033" not in stdout + assert "[bold" not in stdout + assert "[cyan" not in stdout + assert "│" not in stdout + assert "╭" not in stdout + assert "Indexing repository..." not in stdout + + def test_json_with_include_context(self, sample_repo: Path, capsys: pytest.CaptureFixture): + exit_code = main(["authenticate", "--repo", str(sample_repo), "--json", "--include-context"]) + assert exit_code == 0 + captured = capsys.readouterr() + data = json.loads(captured.out) + + assert "rendered_context" in data + assert isinstance(data["rendered_context"], str) + assert "# File: src/auth.py" in data["rendered_context"] + + def test_json_without_include_context_omits_rendered_context(self, sample_repo: Path, capsys: pytest.CaptureFixture): + exit_code = main(["authenticate", "--repo", str(sample_repo), "--json"]) + assert exit_code == 0 + captured = capsys.readouterr() + data = json.loads(captured.out) + + # rendered_context should not bloat response by default + assert "rendered_context" not in data + + def test_json_with_output_file(self, sample_repo: Path, tmp_path: Path, capsys: pytest.CaptureFixture): + out_file = tmp_path / "saved_context.md" + exit_code = main([ + "authenticate", + "--repo", str(sample_repo), + "--json", + "--output-file", str(out_file) + ]) + assert exit_code == 0 + captured = capsys.readouterr() + + # Stdout must remain pure valid JSON with no progress or file save messages + data = json.loads(captured.out) + assert data["status"] == "success" + + # File must be written on disk + assert out_file.exists() + saved_text = out_file.read_text(encoding="utf-8") + assert len(saved_text) > 0 + assert "src/auth.py" in saved_text + + def test_json_on_empty_repo(self, tmp_path: Path, capsys: pytest.CaptureFixture): + empty_repo = tmp_path / "empty_repo" + empty_repo.mkdir() + + exit_code = main(["find something", "--repo", str(empty_repo), "--json"]) + assert exit_code == 0 + captured = capsys.readouterr() + data = json.loads(captured.out) + + assert data["schema_version"] == "1.2.0" + assert data["status"] == "abstained" + assert data["abstained"] is True + assert data["selected_files"] == [] + assert data["total_tokens"] == 0 + + def test_legacy_mode_unchanged_when_json_not_passed(self, sample_repo: Path, capsys: pytest.CaptureFixture): + exit_code = main(["authenticate", "--repo", str(sample_repo)]) + assert exit_code == 0 + captured = capsys.readouterr() + stdout = captured.out + + # Legacy mode prints Rich tables, not pure JSON + with pytest.raises(json.JSONDecodeError): + json.loads(stdout) + + assert "Retrieved Context" in stdout + assert "src/auth.py" in stdout + + def test_invalid_repo_returns_exit_code_1_and_stderr(self, tmp_path: Path, capsys: pytest.CaptureFixture): + non_existent = tmp_path / "does_not_exist" + exit_code = main(["query", "--repo", str(non_existent), "--json"]) + assert exit_code == 1 + captured = capsys.readouterr() + + assert captured.out == "" + assert "does not exist or is not a directory" in captured.err diff --git a/tests/test_doc_filter_validation.py b/tests/test_doc_filter_validation.py new file mode 100644 index 0000000..e0a0609 --- /dev/null +++ b/tests/test_doc_filter_validation.py @@ -0,0 +1,129 @@ +""" +Milestone 2B: documentation-filter validation harness and filter behaviour. + +Covers the existing (unchanged) engine filter semantics, false-negative behaviour for gold +documentation files, the statistical helpers of the experiment, and integrity of the frozen +pre-registered split. +""" + +import json +import sys +from pathlib import Path + +import numpy as np +import pytest + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT / "benchmarks")) + +import run_doc_filter_validation as V # noqa: E402 +from reponeedle.engine import RepoNeedleEngine # noqa: E402 +from reponeedle.models import AblationConfig, FileDoc # noqa: E402 + +PHASE2 = ROOT / "reports" / "experimental" / "phase2_documentation_filter" + +CONTENT = "class SessionManager:\n def refresh_token(self):\n return 'session timeout refresh'\n" + + +def _engine(): + docs = [ + FileDoc(path="docs/session.md", content="# Session timeout refresh guide\n" + CONTENT), + FileDoc(path="README.rst", content="Session timeout refresh token overview\n" + CONTENT), + FileDoc(path="src/session.py", content=CONTENT), + FileDoc(path="src/other.py", content="def unrelated():\n return 42\n"), + ] + return RepoNeedleEngine.from_documents(docs) + + +def _ranked(engine, cfg): + return engine.retrieve("session timeout refresh token", budget_tokens=4000, ablation_config=cfg).ranked_files + + +class TestFilterSemantics: + + def test_filter_is_off_by_default(self): + cfg = AblationConfig() + assert cfg.enable_doc_filter is False + assert cfg.doc_filter_penalty == 0.5 + + @pytest.mark.parametrize("path,expected", [ + ("docs/guide.md", True), ("README.rst", True), ("CHANGELOG.txt", True), (".github/workflows/ci.yml", True), + ("site/index.html", True), ("src/tutorial/intro.py", True), ("pkg/doc/conf.py", True), + ("src/session.py", False), ("tests/test_docs_helpers.py", False), ("Makefile", False), + ]) + def test_predicate_replica(self, path, expected): + assert V.is_doc(path) is expected + + def test_predicate_is_broader_than_documentation(self): + """Documented limitation: the existing path predicate also matches non-documentation files.""" + assert V.is_doc("requirements.txt") and V.is_doc("CMakeLists.txt") and V.is_doc("lib/pydoc/x.py") + + def test_penalty_zero_demotes_docs_below_matching_code(self): + e = _engine() + on = _ranked(e, AblationConfig(enable_doc_filter=True, doc_filter_penalty=0.0)) + assert on.index("src/session.py") < on.index("docs/session.md") + assert on.index("src/session.py") < on.index("README.rst") + + def test_penalty_one_is_a_no_op(self): + e = _engine() + assert _ranked(e, AblationConfig()) == _ranked(e, AblationConfig(enable_doc_filter=True, doc_filter_penalty=1.0)) + + def test_gold_documentation_is_demoted_never_removed(self): + """False-negative regression: the filter must keep documentation files in the ranking.""" + e = _engine() + for pen in (0.5, 0.25, 0.0): + ranked = _ranked(e, AblationConfig(enable_doc_filter=True, doc_filter_penalty=pen)) + assert {"docs/session.md", "README.rst"} <= set(ranked) + assert len(ranked) == 4 + + def test_off_arm_with_explicit_config_matches_default(self): + e = _engine() + default = e.retrieve("session timeout refresh token", budget_tokens=8000) + explicit = e.retrieve("session timeout refresh token", budget_tokens=8000, ablation_config=AblationConfig()) + assert default.ranked_files == explicit.ranked_files + assert [f.path for f in default.selected_files] == [f.path for f in explicit.selected_files] + + +class TestStatistics: + + def test_holm_known_example(self): + adj = V.holm([0.01, 0.04, 0.03, 0.005]) + assert adj == pytest.approx([0.03, 0.06, 0.06, 0.02]) + + def test_holm_is_monotone_and_capped(self): + adj = V.holm([0.5, 0.9, 0.01]) + assert max(adj) <= 1.0 and adj[2] == pytest.approx(0.03) + + def test_regression_p_value_direction(self): + clusters = [f"r{i % 5}" for i in range(50)] + _, p_harm = V.bootstrap([-0.1] * 50, clusters, n_boot=500) + _, p_gain = V.bootstrap([+0.1] * 50, clusters, n_boot=500) + assert p_harm < 0.01 and p_gain > 0.99 + + def test_canonical_hash_is_order_independent(self): + assert V.canonical_hash({"b": 1, "a": [1, 2]}) == V.canonical_hash({"a": [1, 2], "b": 1}) + + +@pytest.mark.skipif(not (PHASE2 / "split.json").exists(), reason="pre-registered split not generated") +class TestFrozenSplit: + + def test_split_hash_matches_record_and_protocol(self): + split = V.load_split(verify=True) + assert split["seed"] == V.PROTOCOL["split"]["seed"] + + def test_repositories_assigned_exactly_once_and_stratified(self): + split = json.loads((PHASE2 / "split.json").read_text(encoding="utf-8")) + assign = split["assignment"] + assert len(assign) == 25 and set(assign.values()) == {"dev", "test"} + assert sum(v == "test" for v in assign.values()) == V.PROTOCOL["split"]["test_repositories"] + for task, info in split["repositories_per_task"].items(): + assert info["dev"] >= 1 and info["test"] >= 1 + assert abs(info["test"] - info["target_test"]) <= 1 + + def test_results_never_cross_splits(self): + split = json.loads((PHASE2 / "split.json").read_text(encoding="utf-8")) + for phase in ("dev", "test"): + for p in (PHASE2 / phase).glob("shard_*.jsonl") if (PHASE2 / phase).exists() else []: + for line in open(p, encoding="utf-8"): + r = json.loads(line) + assert split["assignment"][r["repo"]] == phase diff --git a/tests/test_documentation_consistency.py b/tests/test_documentation_consistency.py new file mode 100644 index 0000000..a527f3e --- /dev/null +++ b/tests/test_documentation_consistency.py @@ -0,0 +1,265 @@ +""" +Milestone 1D: guards against documentation drifting away from the implementation. + +These checks validate real configuration and invocation behaviour (CLI parsers, pyproject +metadata, benchmark abstention policy, MCP tool schemas) and look only for a small set of +specific, known-bad claim patterns. They deliberately avoid asserting exact prose. +""" + +import asyncio +import pathlib +import re +import shlex +import sys + +import pytest + +ROOT = pathlib.Path(__file__).resolve().parent.parent + +if sys.version_info >= (3, 11): + import tomllib +else: # pragma: no cover - Python 3.10 uses the declared tomli dependency + import tomli as tomllib + +PYPROJECT = tomllib.loads((ROOT / "pyproject.toml").read_text(encoding="utf-8")) +BENCH_CONFIG = ROOT / "docs" / "BENCHMARK_CONFIGURATION.md" + +# Living documents that describe the *current* state (historical reports are excluded on purpose). +CURRENT_DOCS = [ + ROOT / "README.md", + ROOT / "KNOWN_LIMITATIONS.md", + ROOT / "CLAIMS_AND_EVIDENCE.md", + ROOT / "docs" / "MCP_INTEGRATION.md", + ROOT / "docs" / "ROADMAP.md", + ROOT / "docs" / "BENCHMARK_CONFIGURATION.md", + ROOT / "docs" / "ANNOUNCEMENT.md", # historical, but corrected in place and publicly linked +] +USER_FACING_DOCS = [ROOT / "README.md", ROOT / "docs" / "MCP_INTEGRATION.md"] + + +def _read(path: pathlib.Path) -> str: + return path.read_text(encoding="utf-8") + + +def _table_paths(doc: str, block: str) -> list: + m = re.search(rf"(.*?)", doc, re.S) + assert m, f"marker block '{block}' missing from docs/BENCHMARK_CONFIGURATION.md" + return re.findall(r"^\|\s*`([^`]+\.py)`", m.group(1), re.M) + + +def _code_blocks(text: str, langs=("bash", "sh", "")) -> list: + return [body for lang, body in re.findall(r"```(\w*)\n(.*?)```", text, re.S) if lang in langs] + + +# --------------------------------------------------------------------------- +# Benchmark abstention policy +# --------------------------------------------------------------------------- + +class TestBenchmarkAbstentionPolicy: + + def test_documented_opt_in_scripts_really_opt_in(self): + paths = _table_paths(_read(BENCH_CONFIG), "abstention-opt-in-scripts") + assert paths, "opt-in table is empty" + for rel in paths: + src = (ROOT / rel) + assert src.is_file(), f"{rel} listed in BENCHMARK_CONFIGURATION.md does not exist" + text = _read(src) + assert re.search(r"enable_experimental_abstention\s*=\s*(True|is_selective)\b", text), ( + f"{rel} is documented as enabling experimental abstention but never does" + ) + + def test_every_script_reading_engine_abstention_is_classified(self): + """A new script that reads `.abstained` from RepoNeedleEngine results must declare a policy.""" + doc = _read(BENCH_CONFIG) + classified = set(_table_paths(doc, "abstention-opt-in-scripts")) | set( + _table_paths(doc, "abstention-default-exempt") + ) + unclassified = [] + for d in ("benchmarks", "research", "scripts"): + for p in (ROOT / d).rglob("*.py"): + text = p.read_text(encoding="utf-8", errors="ignore") + if "RepoNeedleEngine" in text and re.search(r"\b\w+\.abstained\b", text): + rel = p.relative_to(ROOT).as_posix() + if rel not in classified: + unclassified.append(rel) + assert not unclassified, ( + "These scripts read RepoNeedleEngine abstention results but are not listed in " + f"docs/BENCHMARK_CONFIGURATION.md (opt-in or exempt table): {unclassified}" + ) + + def test_synthetic_runner_enables_heuristic_only_for_selective_samples(self, monkeypatch): + """Invokes the real benchmark harness and records how it calls the engine.""" + sys.path.insert(0, str(ROOT)) + import benchmarks.runner as runner_mod + from reponeedle.engine import RepoNeedleEngine + from reponeedle.models import TaskType + + calls = [] + original = RepoNeedleEngine.retrieve + + def spy(self, query, *args, **kwargs): + task = getattr(query, "task_type", None) + calls.append((task, kwargs.get("enable_experimental_abstention"))) + return original(self, query, *args, **kwargs) + + monkeypatch.setattr(RepoNeedleEngine, "retrieve", spy) + bench = runner_mod.BenchmarkRunner() + selective = [s for s in bench.samples if s.task_type == TaskType.SELECTIVE][:1] + positive = [s for s in bench.samples if s.task_type != TaskType.SELECTIVE][:1] + assert selective and positive + bench.samples = selective + positive + result = bench.run_all() + + sel_calls = [flag for task, flag in calls if task == TaskType.SELECTIVE] + pos_calls = [flag for task, flag in calls if task != TaskType.SELECTIVE] + assert sel_calls and all(flag is True for flag in sel_calls) + assert pos_calls and all(not flag for flag in pos_calls) + assert "SelectiveAccuracy" in result["RepoNeedle"] + + def test_reproducibility_script_reports_missing_data(self, monkeypatch, tmp_path): + sys.path.insert(0, str(ROOT / "scripts")) + import verify_abstention_reproducibility as v + + monkeypatch.setattr(v, "ARB_DIR", tmp_path / "missing_arb") + monkeypatch.setattr(v, "REPORT_DIR", tmp_path / "missing_reports") + assert v.main([]) == 2 + with pytest.raises(SystemExit) as exc: + v.main(["--help"]) + assert exc.value.code == 0 + + +# --------------------------------------------------------------------------- +# Abstention default and stale claims +# --------------------------------------------------------------------------- + +class TestAbstentionClaims: + + def test_default_is_disabled_and_documented_as_such(self): + from reponeedle.models import BudgetConfig + assert BudgetConfig().enable_experimental_abstention is False + for doc in (ROOT / "README.md", ROOT / "KNOWN_LIMITATIONS.md", ROOT / "docs" / "MCP_INTEGRATION.md"): + assert "disabled by default" in _read(doc).lower(), f"{doc.name} does not state the default" + + def test_documented_engine_threshold_matches_code(self): + from reponeedle.models import BudgetConfig + threshold = BudgetConfig().abstention_threshold + for doc in (ROOT / "KNOWN_LIMITATIONS.md", ROOT / "CLAIMS_AND_EVIDENCE.md"): + text = _read(doc) + assert f"{threshold}" in text, f"{doc.name} does not mention the engine threshold {threshold}" + for line in text.splitlines(): + if re.search(r"default abstention threshold[^.]*0\.38", line, re.I): + assert re.search(r"correct|not used|unused", line, re.I), f"stale threshold claim: {line}" + + @pytest.mark.parametrize("pattern", [ + r"calibrated selective abstention", + r"100\s*%\s*selective abstention", + r"100\s*%\s*test coverage", + r"production[- ]ready in (reponeedle )?1\.2", + ]) + def test_known_false_claims_only_appear_as_corrections(self, pattern): + correction = re.compile(r"retract|refut|incorrect|correct(ed|ion)|not (supported|calibrated)", re.I) + offenders = [] + for doc in CURRENT_DOCS: + for no, line in enumerate(_read(doc).splitlines(), start=1): + if re.search(pattern, line, re.I) and not correction.search(line): + offenders.append(f"{doc.relative_to(ROOT)}:{no}: {line.strip()[:120]}") + assert not offenders, "Uncorrected false claim(s):\n" + "\n".join(offenders) + + +# --------------------------------------------------------------------------- +# Release status, test counts, installation examples +# --------------------------------------------------------------------------- + +class TestReleaseAndInstallDocs: + + def test_package_version_is_consistent(self): + import reponeedle + assert PYPROJECT["project"]["version"] == reponeedle.__version__ + + def test_unreleased_version_is_never_described_as_released(self): + version = tuple(int(x) for x in PYPROJECT["project"]["version"].split(".")[:2]) + if version >= (1, 2): + pytest.skip("1.2 is the packaged version; release wording is no longer forward-looking") + qualifier = re.compile( + r"planned|unreleased|not yet|development branch|until|once|upcoming|pending|candidate", re.I + ) + offenders = [] + for doc in CURRENT_DOCS: + for no, line in enumerate(_read(doc).splitlines(), start=1): + if re.search(r"\b1\.2(\.0)?\b", line) and not qualifier.search(line): + if re.search(r"releas|availab|ship|production|since|in 1\.2", line, re.I): + offenders.append(f"{doc.relative_to(ROOT)}:{no}: {line.strip()[:120]}") + assert not offenders, "Lines presenting unreleased 1.2 as released:\n" + "\n".join(offenders) + + def test_no_hard_coded_test_counts_in_user_docs(self): + for doc in [ROOT / "README.md", *sorted((ROOT / "docs").glob("*.md"))]: + m = re.search(r"\b\d{2,4}\s+(?:tests?\s+)?passed\b", _read(doc)) + assert not m, f"{doc.relative_to(ROOT)} hard-codes a test count ('{m.group(0)}'); it will go stale" + + def test_python_requirement_matches_docs(self): + assert PYPROJECT["project"]["requires-python"] == ">=3.10" + assert "3.10" in _read(ROOT / "README.md") + assert "3.10" in _read(ROOT / "docs" / "MCP_INTEGRATION.md") + + def test_documented_extras_exist(self): + extras = set(PYPROJECT["project"].get("optional-dependencies", {})) + for doc in USER_FACING_DOCS: + for extra in re.findall(r"(?:reponeedle|\.)\[([a-z0-9_-]+)\]", _read(doc)): + assert extra in extras, f"{doc.name} references unknown extra '[{extra}]'" + + def test_documented_console_scripts_exist(self): + scripts = set(PYPROJECT["project"]["scripts"]) + for doc in USER_FACING_DOCS: + for block in _code_blocks(_read(doc)): + for line in block.splitlines(): + tok = line.strip().split(" ", 1)[0] + if tok.startswith("reponeedle"): + assert tok in scripts, f"{doc.name} uses unknown command '{tok}'" + + def test_documented_cli_flags_exist(self): + from reponeedle.cli import build_parser as cli_parser + from reponeedle.mcp.server import build_parser as mcp_parser_fn + parsers = { + "reponeedle": cli_parser(), + "reponeedle-mcp": mcp_parser_fn(), + "python -m reponeedle.mcp": mcp_parser_fn(), + } + known = {name: {o for a in p._actions for o in a.option_strings} for name, p in parsers.items()} + checked = 0 + for doc in USER_FACING_DOCS: + for block in _code_blocks(_read(doc)): + for line in block.splitlines(): + line = line.split("#", 1)[0].strip() + for prefix, opts in sorted(known.items(), key=lambda kv: -len(kv[0])): + if line.startswith(prefix + " ") or line == prefix: + for tok in shlex.split(line, posix=True): + flag = tok.split("=", 1)[0] + if flag.startswith("--"): + checked += 1 + assert flag in opts, f"{doc.name}: '{flag}' is not a '{prefix}' option" + break + assert checked > 5, "expected to validate CLI flags from documentation examples" + + +# --------------------------------------------------------------------------- +# MCP documentation vs registered tools +# --------------------------------------------------------------------------- + +class TestMCPDocs: + + def test_documented_tools_and_arguments_exist(self, tmp_path): + pytest.importorskip("mcp") + from reponeedle.mcp.server import create_mcp_server + + (tmp_path / "a.py").write_text("def f():\n return 1\n", encoding="utf-8") + app = create_mcp_server(repo_dir=str(tmp_path)) + tools = {t.name: t for t in asyncio.run(app.list_tools())} + doc = _read(ROOT / "docs" / "MCP_INTEGRATION.md") + + documented = set(re.findall(r"`(reponeedle_[a-z_]+)`", doc)) + assert documented == set(tools), f"documented {sorted(documented)} vs registered {sorted(tools)}" + + retrieve_props = set(tools["reponeedle_retrieve"].inputSchema["properties"]) + section = doc.split("### 5.1 `reponeedle_retrieve`", 1)[1].split("### 5.2", 1)[0] + doc_args = set(re.findall(r"^- `([a-z_]+)` \(\*", section, re.M)) + assert doc_args == retrieve_props, f"documented {sorted(doc_args)} vs schema {sorted(retrieve_props)}" diff --git a/tests/test_engine.py b/tests/test_engine.py index 666b1c0..0261785 100644 --- a/tests/test_engine.py +++ b/tests/test_engine.py @@ -28,8 +28,13 @@ def test_engine_end_to_end_retrieval(): paths = [f.path for f in res_test.selected_files] assert "tests/test_auth.py" in paths - # 3. Out-of-repo counterfactual query should abstain - res_abs = engine.retrieve("Cargo.toml rust tokio async runtime memory leak", budget_tokens=2000) + # 3. Out-of-repo counterfactual query should abstain when the experimental heuristic is + # explicitly enabled (opt-in since 1.2.0; see test_abstention_opt_in.py for the default). + res_abs = engine.retrieve( + "Cargo.toml rust tokio async runtime memory leak", + budget_tokens=2000, + enable_experimental_abstention=True, + ) assert res_abs.abstained assert len(res_abs.selected_files) == 0 diff --git a/tests/test_indexing_diagnostics.py b/tests/test_indexing_diagnostics.py new file mode 100644 index 0000000..19ab10d --- /dev/null +++ b/tests/test_indexing_diagnostics.py @@ -0,0 +1,332 @@ +""" +Milestone 1C: configurable file-size limit and indexing diagnostics. + +Covers the Python API, CLI and MCP surfaces: +- Default 500,000-byte limit, custom limits, exact-boundary behavior. +- Invalid limits rejected with clear exceptions. +- Skipped paths recorded deterministically, never file contents. +- Warnings go to stderr / logging, never to JSON stdout. +""" + +import json +import logging +import subprocess +import sys +from pathlib import Path + +import pytest + +from reponeedle.cli import main +from reponeedle.engine import ( + DEFAULT_MAX_FILE_SIZE_BYTES, + RepoNeedleEngine, + validate_max_file_size_bytes, +) +from reponeedle.models import FileDoc, IndexingDiagnostics + +SECRET_MARKER = "SECRET_TOKEN_DO_NOT_LEAK_8f3a2c" + + +def _write_exact(path: Path, size: int, marker: str = "") -> None: + """Writes a Python file of exactly `size` bytes (binary write: no newline translation).""" + path.parent.mkdir(parents=True, exist_ok=True) + head = f"# {marker}\n".encode("ascii") if marker else b"# filler\n" + assert size >= len(head) + path.write_bytes(head + b"x" * (size - len(head))) + + +@pytest.fixture +def sized_repo(tmp_path: Path) -> Path: + """Repo with a normal file, one exactly at 1000 bytes and two over 1000 bytes.""" + repo = tmp_path / "sized_repo" + (repo / "src").mkdir(parents=True) + (repo / "src" / "auth.py").write_text( + "class AuthService:\n def authenticate(self, token):\n return True\n", + encoding="utf-8", + ) + _write_exact(repo / "src" / "at_limit.py", 1000) + _write_exact(repo / "vendor" / "huge_source.py", 1001, marker=SECRET_MARKER) + _write_exact(repo / "generated" / "large_module.py", 5000, marker=SECRET_MARKER) + return repo + + +# --------------------------------------------------------------------------- +# Validation +# --------------------------------------------------------------------------- + +class TestLimitValidation: + + def test_default_constant(self): + assert DEFAULT_MAX_FILE_SIZE_BYTES == 500_000 + + @pytest.mark.parametrize("bad", [-1, -500_000, 0]) + def test_non_positive_rejected(self, bad, tmp_path: Path): + with pytest.raises(ValueError, match="positive integer"): + validate_max_file_size_bytes(bad) + with pytest.raises(ValueError, match="positive integer"): + RepoNeedleEngine.from_directory(str(tmp_path), max_file_size_bytes=bad) + + @pytest.mark.parametrize("bad", [1.5, "1000", None, True]) + def test_non_integer_rejected(self, bad, tmp_path: Path): + with pytest.raises(TypeError, match="positive integer"): + RepoNeedleEngine.from_directory(str(tmp_path), max_file_size_bytes=bad) + + def test_valid_limits_accepted(self): + assert validate_max_file_size_bytes(1) == 1 + assert validate_max_file_size_bytes(10_000_000) == 10_000_000 + + +# --------------------------------------------------------------------------- +# Python API +# --------------------------------------------------------------------------- + +class TestDirectoryIndexing: + + def test_default_limit_boundaries(self, tmp_path: Path): + repo = tmp_path / "default_repo" + _write_exact(repo / "exactly_default.py", DEFAULT_MAX_FILE_SIZE_BYTES) + _write_exact(repo / "over_default.py", DEFAULT_MAX_FILE_SIZE_BYTES + 1) + + engine = RepoNeedleEngine.from_directory(str(repo)) + diag = engine.indexing_diagnostics + indexed = {d.path for d in engine.docs} + + assert diag.max_file_size_bytes == 500_000 + assert "exactly_default.py" in indexed + assert "over_default.py" not in indexed + assert diag.skipped_paths == ["over_default.py"] + assert diag.skipped_large_files == 1 + assert diag.indexed_files == 1 + + def test_custom_limit(self, sized_repo: Path): + engine = RepoNeedleEngine.from_directory(str(sized_repo), max_file_size_bytes=1000) + diag = engine.indexing_diagnostics + indexed = {d.path for d in engine.docs} + + assert diag.source == "directory" + assert diag.max_file_size_bytes == 1000 + # File exactly at the limit is indexed; strictly larger files are skipped. + assert "src/at_limit.py" in indexed + assert "vendor/huge_source.py" not in indexed + assert "generated/large_module.py" not in indexed + assert diag.skipped_large_files == 2 + assert diag.skipped_paths == ["generated/large_module.py", "vendor/huge_source.py"] + assert diag.indexed_files == len(engine.docs) == 2 + assert diag.skipped_unreadable_files == 0 + assert diag.max_files_reached is False + + def test_raising_limit_indexes_previously_skipped_files(self, sized_repo: Path): + engine = RepoNeedleEngine.from_directory(str(sized_repo), max_file_size_bytes=10_000) + assert engine.indexing_diagnostics.skipped_paths == [] + assert engine.indexing_diagnostics.indexed_files == 4 + + def test_skipped_paths_are_repo_relative_forward_slash(self, sized_repo: Path): + engine = RepoNeedleEngine.from_directory(str(sized_repo), max_file_size_bytes=1000) + for p in engine.indexing_diagnostics.skipped_paths: + assert "\\" not in p + assert not Path(p).is_absolute() + assert (sized_repo / p).is_file() + + def test_no_file_contents_in_diagnostics(self, sized_repo: Path): + engine = RepoNeedleEngine.from_directory(str(sized_repo), max_file_size_bytes=1000) + dumped = json.dumps(engine.indexing_diagnostics.model_dump()) + assert SECRET_MARKER not in dumped + assert "xxxxxxxx" not in dumped + + def test_deterministic_diagnostics(self, sized_repo: Path): + a = RepoNeedleEngine.from_directory(str(sized_repo), max_file_size_bytes=1000) + b = RepoNeedleEngine.from_directory(str(sized_repo), max_file_size_bytes=1000) + assert a.indexing_diagnostics.model_dump() == b.indexing_diagnostics.model_dump() + + def test_warning_logged_without_paths_or_contents(self, sized_repo: Path, caplog): + with caplog.at_level(logging.WARNING, logger="reponeedle.engine"): + RepoNeedleEngine.from_directory(str(sized_repo), max_file_size_bytes=1000) + records = [r for r in caplog.records if r.name == "reponeedle.engine"] + assert len(records) == 1 + msg = records[0].getMessage() + assert "skipped 2 file(s)" in msg + assert "max_file_size_bytes=1000" in msg + assert SECRET_MARKER not in caplog.text + assert "huge_source" not in caplog.text + + def test_no_warning_when_nothing_skipped(self, sized_repo: Path, caplog): + with caplog.at_level(logging.WARNING, logger="reponeedle.engine"): + RepoNeedleEngine.from_directory(str(sized_repo)) + assert [r for r in caplog.records if r.name == "reponeedle.engine"] == [] + + def test_ignored_and_unsupported_files_not_reported(self, tmp_path: Path): + repo = tmp_path / "ignore_repo" + _write_exact(repo / "keep.py", 50) + _write_exact(repo / "ignored_dir" / "big.py", 5000) + (repo / "blob.bin").write_bytes(b"\0" * 5000) + (repo / ".gitignore").write_text("ignored_dir/\n", encoding="utf-8") + + engine = RepoNeedleEngine.from_directory(str(repo), max_file_size_bytes=1000) + assert engine.indexing_diagnostics.skipped_paths == [] + assert [d.path for d in engine.docs] == ["keep.py"] + + def test_max_files_reached_recorded(self, sized_repo: Path): + engine = RepoNeedleEngine.from_directory(str(sized_repo), max_files=1, max_file_size_bytes=10_000) + diag = engine.indexing_diagnostics + assert diag.max_files == 1 + assert diag.max_files_reached is True + assert diag.indexed_files == 1 + + def test_empty_directory(self, tmp_path: Path): + engine = RepoNeedleEngine.from_directory(str(tmp_path)) + diag = engine.indexing_diagnostics + assert diag.indexed_files == 0 + assert diag.skipped_paths == [] + assert diag.max_files_reached is False + + +class TestDocumentIndexing: + + def test_from_documents_source(self): + engine = RepoNeedleEngine.from_documents([FileDoc(path="a.py", content="x = 1\n")]) + diag = engine.indexing_diagnostics + assert diag.source == "documents" + assert diag.indexed_files == 1 + assert diag.max_file_size_bytes is None + assert diag.skipped_paths == [] + + def test_update_documents_refreshes_count(self): + engine = RepoNeedleEngine.from_documents([FileDoc(path="a.py", content="x = 1\n")]) + engine.update_documents([FileDoc(path="b.py", content="y = 2\n")]) + assert engine.indexing_diagnostics.indexed_files == 2 + + def test_model_defaults(self): + diag = IndexingDiagnostics() + assert diag.indexed_files == 0 + assert diag.skipped_large_files == 0 + assert diag.skipped_paths == [] + + +# --------------------------------------------------------------------------- +# CLI +# --------------------------------------------------------------------------- + +class TestCLIIndexing: + + def test_json_includes_indexing_diagnostics(self, sized_repo: Path, capsys): + exit_code = main([ + "authenticate", "--repo", str(sized_repo), "--json", "--max-file-size-bytes", "1000", + ]) + assert exit_code == 0 + captured = capsys.readouterr() + data = json.loads(captured.out) # stdout is strictly valid JSON + + idx = data["indexing"] + assert idx["max_file_size_bytes"] == 1000 + assert idx["skipped_large_files"] == 2 + assert idx["skipped_paths"] == ["generated/large_module.py", "vendor/huge_source.py"] + assert idx["indexed_files"] == 2 + assert SECRET_MARKER not in captured.out + assert SECRET_MARKER not in captured.err + + def test_json_default_limit_reported(self, sized_repo: Path, capsys): + assert main(["authenticate", "--repo", str(sized_repo), "--json"]) == 0 + captured = capsys.readouterr() + data = json.loads(captured.out) + assert data["indexing"]["max_file_size_bytes"] == 500_000 + assert data["indexing"]["skipped_paths"] == [] + assert captured.err == "" + + @pytest.mark.parametrize("bad", ["0", "-5"]) + def test_invalid_limit_exits_1(self, sized_repo: Path, capsys, bad): + exit_code = main(["authenticate", "--repo", str(sized_repo), "--json", f"--max-file-size-bytes={bad}"]) + assert exit_code == 1 + captured = capsys.readouterr() + assert captured.out == "" + assert "Error:" in captured.err + assert "positive integer" in captured.err + + def test_rich_mode_notice_goes_to_stderr(self, sized_repo: Path, capsys): + exit_code = main(["authenticate", "--repo", str(sized_repo), "--max-file-size-bytes", "1000"]) + assert exit_code == 0 + captured = capsys.readouterr() + assert "Indexing notice" in captured.err + assert "vendor/huge_source.py" in captured.err + assert "Indexing notice" not in captured.out + assert SECRET_MARKER not in captured.err + + def test_subprocess_json_stdout_clean_and_warning_on_stderr(self, sized_repo: Path): + proc = subprocess.run( + [sys.executable, "-m", "reponeedle.cli", "authenticate", "--repo", str(sized_repo), + "--json", "--max-file-size-bytes", "1000"], + capture_output=True, text=True, encoding="utf-8", timeout=120, + ) + assert proc.returncode == 0, proc.stderr + data = json.loads(proc.stdout) + assert data["indexing"]["skipped_large_files"] == 2 + assert "skipped 2 file(s)" in proc.stderr + assert SECRET_MARKER not in proc.stdout + assert SECRET_MARKER not in proc.stderr + + +# --------------------------------------------------------------------------- +# MCP +# --------------------------------------------------------------------------- + +def _extract_payload(res) -> dict: + if isinstance(res, tuple): + if len(res) > 1 and isinstance(res[1], dict) and "result" in res[1]: + return res[1]["result"] + return json.loads(res[0][0].text) + if isinstance(res, list): + return json.loads(res[0].text) + return json.loads(res.text) + + +class TestMCPIndexing: + + @pytest.fixture(autouse=True) + def _require_mcp(self): + pytest.importorskip("mcp") + + @pytest.mark.asyncio + async def test_inspect_reports_indexing_statistics(self, sized_repo: Path): + from reponeedle.mcp.server import create_mcp_server + app = create_mcp_server(repo_dir=str(sized_repo), max_file_size_bytes=1000) + data = _extract_payload(await app.call_tool("reponeedle_inspect", {})) + + idx = data["indexing"] + assert idx["source"] == "directory" + assert idx["max_file_size_bytes"] == 1000 + assert idx["skipped_large_files"] == 2 + assert idx["skipped_paths"] == ["generated/large_module.py", "vendor/huge_source.py"] + assert idx["indexed_files"] == data["indexed_file_count"] == 2 + assert SECRET_MARKER not in json.dumps(data) + + @pytest.mark.asyncio + async def test_inspect_default_limit(self, sized_repo: Path): + from reponeedle.mcp.server import create_mcp_server + app = create_mcp_server(repo_dir=str(sized_repo)) + data = _extract_payload(await app.call_tool("reponeedle_inspect", {})) + assert data["indexing"]["max_file_size_bytes"] == 500_000 + assert data["indexing"]["skipped_paths"] == [] + + def test_startup_stderr_reports_exclusions(self, sized_repo: Path, capsys): + from reponeedle.mcp.server import create_mcp_server + create_mcp_server(repo_dir=str(sized_repo), max_file_size_bytes=1000) + captured = capsys.readouterr() + assert captured.out == "" + assert "Indexing exclusions: 2 file(s) over 1000 bytes" in captured.err + + @pytest.mark.parametrize("bad", [0, -1]) + def test_create_server_rejects_invalid_limit(self, sized_repo: Path, bad): + from reponeedle.mcp.server import create_mcp_server + with pytest.raises(ValueError, match="positive integer"): + create_mcp_server(repo_dir=str(sized_repo), max_file_size_bytes=bad) + + def test_server_main_rejects_invalid_limit(self, sized_repo: Path, capsys): + from reponeedle.mcp.server import main as mcp_main + assert mcp_main(["--repo", str(sized_repo), "--max-file-size-bytes", "-5"]) == 1 + assert "positive integer" in capsys.readouterr().err + + def test_server_config_validates_limit(self): + from pydantic import ValidationError + from reponeedle.mcp.config import MCPServerConfig + assert MCPServerConfig().max_file_size_bytes == 500_000 + with pytest.raises(ValidationError): + MCPServerConfig(max_file_size_bytes=0) diff --git a/tests/test_indexing_security.py b/tests/test_indexing_security.py new file mode 100644 index 0000000..a5e5f07 --- /dev/null +++ b/tests/test_indexing_security.py @@ -0,0 +1,194 @@ +""" +Release blocker B1: indexing must never read files whose canonical location is outside the +repository root (symlinks; junctions on Windows), and anchors must apply the same containment. + +Symlink-dependent tests run on Linux CI. Where symlink creation is not permitted they skip, unless +REPONEEDLE_FAIL_ON_SKIP=1 (the Linux MCP/security CI job), where a skip fails the run. +Directory-link tests fall back to NTFS junctions on Windows, which need no special privilege. +""" + +import json +import os +import sys +from pathlib import Path + +import pytest + +from reponeedle import RepoNeedleEngine +from reponeedle.cli import main as cli_main, resolve_and_validate_anchors +from reponeedle.engine import is_path_within_root, relpath_escapes_root, resolve_within_root + +SENTINEL = "SENTINEL_OUTSIDE_SECRET_5b1f9e" + + +def _file_symlink(link: Path, target: Path): + try: + os.symlink(str(target), str(link)) + except (OSError, NotImplementedError): + pytest.skip("file symlink creation not permitted in this environment") + + +def _dir_link(link: Path, target: Path): + try: + os.symlink(str(target), str(link), target_is_directory=True) + return "symlink" + except (OSError, NotImplementedError): + if sys.platform == "win32": + import _winapi + _winapi.CreateJunction(str(target), str(link)) + return "junction" + pytest.skip("directory symlink creation not permitted in this environment") + + +@pytest.fixture +def layout(tmp_path: Path): + outside = tmp_path / "outside" + outside.mkdir() + (outside / "secret.py").write_text(f"API_TOKEN = '{SENTINEL}'\n", encoding="utf-8") + repo = tmp_path / "repo" + (repo / "src").mkdir(parents=True) + (repo / "src" / "app.py").write_text("def handler():\n return 'session token refresh'\n", encoding="utf-8") + (repo / "src" / "real_module.py").write_text("def inside_helper():\n return 'inside'\n", encoding="utf-8") + return repo, outside + + +def _assert_sentinel_unreachable(engine): + blob = json.dumps(engine.indexing_diagnostics.model_dump()) + assert SENTINEL not in blob + assert all(SENTINEL not in d.content for d in engine.docs) + res = engine.retrieve("API_TOKEN secret token", budget_tokens=8000) + assert SENTINEL not in res.rendered_context + assert SENTINEL not in json.dumps(res.model_dump(), default=str) + + +class TestContainmentHelpers: + + def test_within_root_is_component_aware(self, tmp_path: Path): + root = tmp_path / "repo" + root.mkdir() + (tmp_path / "repo2").mkdir() + assert is_path_within_root(str(root / "a" / "b.py"), str(root)) + assert is_path_within_root(str(root), str(root)) + assert not is_path_within_root(str(tmp_path / "repo2" / "x.py"), str(root)) # shared prefix, other dir + assert not is_path_within_root(str(tmp_path / "x.py"), str(root)) + + @pytest.mark.parametrize("rel,escapes", [ + ("../x.py", True), ("..", True), (os.path.join("..", "a", "b"), True), + ("..cache/x.py", False), ("...weird/x.py", False), ("a/../b.py", False), ("src/app.py", False), + ]) + def test_relpath_escapes_root(self, rel, escapes): + assert relpath_escapes_root(rel) is escapes + + def test_resolve_within_root_regular_file(self, layout): + repo, _ = layout + real_root = os.path.realpath(str(repo)) + assert resolve_within_root(str(repo / "src" / "app.py"), real_root) == os.path.realpath(str(repo / "src" / "app.py")) + + +class TestIndexingContainment: + + def test_file_symlink_to_outside_is_not_indexed(self, layout): + repo, outside = layout + _file_symlink(repo / "src" / "creds.py", outside / "secret.py") + engine = RepoNeedleEngine.from_directory(str(repo)) + diag = engine.indexing_diagnostics + assert "src/creds.py" not in {d.path for d in engine.docs} + assert {"src/app.py", "src/real_module.py"} <= {d.path for d in engine.docs} + assert diag.skipped_symlink_outside_repository == 1 + assert diag.symlink_outside_repository_paths == ["src/creds.py"] + _assert_sentinel_unreachable(engine) + + def test_directory_link_to_outside_is_not_indexed(self, layout): + repo, outside = layout + kind = _dir_link(repo / "linked", outside) + engine = RepoNeedleEngine.from_directory(str(repo)) + assert not any(d.path.startswith("linked/") for d in engine.docs) + if kind == "junction": # os.walk descends junctions: the escape is detected and recorded + assert engine.indexing_diagnostics.symlink_outside_repository_paths == ["linked/secret.py"] + _assert_sentinel_unreachable(engine) + + def test_symlink_inside_repository_still_works(self, layout): + """Policy: links that resolve inside the repository are indexed under the link path.""" + repo, _ = layout + _file_symlink(repo / "src" / "alias.py", repo / "src" / "real_module.py") + engine = RepoNeedleEngine.from_directory(str(repo)) + paths = {d.path for d in engine.docs} + assert {"src/alias.py", "src/real_module.py"} <= paths + assert engine.indexing_diagnostics.skipped_symlink_outside_repository == 0 + + def test_broken_symlink_and_loop_are_safe(self, layout): + repo, _ = layout + _file_symlink(repo / "src" / "broken.py", repo / "src" / "does_not_exist.py") + _file_symlink(repo / "src" / "loop_a.py", repo / "src" / "loop_b.py") + _file_symlink(repo / "src" / "loop_b.py", repo / "src" / "loop_a.py") + engine = RepoNeedleEngine.from_directory(str(repo)) + diag = engine.indexing_diagnostics + assert {"src/broken.py", "src/loop_a.py", "src/loop_b.py"} <= set(diag.unreadable_paths) + assert "src/app.py" in {d.path for d in engine.docs} + + def test_cli_json_never_contains_sentinel(self, layout, capsys): + repo, outside = layout + _file_symlink(repo / "src" / "creds.py", outside / "secret.py") + assert cli_main(["API_TOKEN secret", "--repo", str(repo), "--json", "--include-context"]) == 0 + out = capsys.readouterr() + data = json.loads(out.out) + assert SENTINEL not in out.out and SENTINEL not in out.err + assert data["indexing"]["symlink_outside_repository_paths"] == ["src/creds.py"] + + +class TestAnchorContainment: + + def test_anchor_symlink_to_outside_is_rejected(self, layout): + repo, outside = layout + _file_symlink(repo / "src" / "creds.py", outside / "secret.py") + with pytest.raises(ValueError, match="outside repository root"): + resolve_and_validate_anchors(str(repo), ["src/creds.py"]) + + def test_anchor_symlink_inside_is_accepted(self, layout): + repo, _ = layout + _file_symlink(repo / "src" / "alias.py", repo / "src" / "real_module.py") + assert resolve_and_validate_anchors(str(repo), ["src/alias.py"]) == ["src/alias.py"] + + def test_anchor_name_starting_with_dotdot_is_accepted(self, layout): + repo, _ = layout + (repo / "..cache").mkdir() + (repo / "..cache" / "state.py").write_text("STATE = 1\n", encoding="utf-8") + assert resolve_and_validate_anchors(str(repo), ["..cache/state.py"]) == ["..cache/state.py"] + + def test_parent_traversal_still_rejected(self, layout, tmp_path: Path): + repo, _ = layout + (tmp_path / "sibling.py").write_text("x = 1\n", encoding="utf-8") + with pytest.raises(ValueError, match="outside repository root"): + resolve_and_validate_anchors(str(repo), ["../sibling.py"]) + + def test_mcp_path_validation_accepts_dotdot_name(self, layout): + from reponeedle.mcp.config import validate_and_normalize_path + repo, _ = layout + (repo / "..cache").mkdir() + (repo / "..cache" / "state.py").write_text("STATE = 1\n", encoding="utf-8") + assert validate_and_normalize_path(str(repo), "..cache/state.py") == "..cache/state.py" + with pytest.raises(ValueError): + validate_and_normalize_path(str(repo), "../outside/secret.py") + + +class TestMCPContainment: + + @pytest.fixture(autouse=True) + def _require_mcp(self): + pytest.importorskip("mcp") + + @pytest.mark.asyncio + async def test_mcp_retrieve_and_inspect_never_expose_sentinel(self, layout): + from reponeedle.mcp.server import create_mcp_server + repo, outside = layout + _dir_link(repo / "linked", outside) + try: + _file_symlink(repo / "src" / "creds.py", outside / "secret.py") + except pytest.skip.Exception: + if sys.platform != "win32": + raise + app = create_mcp_server(repo_dir=str(repo)) + for name, args in [("reponeedle_retrieve", {"query": "API_TOKEN secret token", "budget_tokens": 8000}), + ("reponeedle_inspect", {})]: + res = await app.call_tool(name, args) + assert SENTINEL not in json.dumps(res, default=str) diff --git a/tests/test_lexical_forensics.py b/tests/test_lexical_forensics.py new file mode 100644 index 0000000..aba80d3 --- /dev/null +++ b/tests/test_lexical_forensics.py @@ -0,0 +1,98 @@ +""" +Milestone 2C: lexical forensics harness (benchmarks/run_lexical_forensics.py). + +The forensic conclusions depend on each harness variant changing exactly one documented factor +relative to the production BM25Index; these tests pin that down. Production code is unchanged. +""" + +import json +import sys +from pathlib import Path + +import numpy as np +import pytest + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT / "benchmarks")) + +import run_lexical_forensics as L # noqa: E402 +from reponeedle.core.lexical import BM25Index # noqa: E402 +from reponeedle.core.tokenizer import CodeTokenizer # noqa: E402 +from reponeedle.models import FileDoc # noqa: E402 + +PHASE3 = ROOT / "reports" / "experimental" / "phase3_lexical_retrieval" + +DOCS = [ + FileDoc(path="src/auth/session.py", content="class SessionManager:\n def refresh_token(self):\n return 'session timeout'\n"), + FileDoc(path="docs/guide.md", content="Session timeout guide. Configure the refresh token and the session."), + FileDoc(path="tests/test_session.py", content="def test_timeout():\n assert SessionManager().refresh_token()\n"), + FileDoc(path="src/util.py", content="def helper():\n return 42\n"), +] +QUERY = json.dumps({"review_comment": "make session timeout configurable", "path": "src/auth/session.py", + "diff_hunk_context": "@@ -1,3 +1,4 @@ refresh_token refresh_token session"}) + + +class TestProductionTokenizerFacts: + + def test_plain_words_are_counted_twice(self): + """Documented forensic finding: the production tokenizer emits plain words twice.""" + assert CodeTokenizer().tokenize_text("session").count("session") == 2 + + def test_production_bm25_ignores_query_term_frequency(self): + idx = BM25Index().fit(DOCS) + assert np.array_equal(idx.score("session timeout"), idx.score("session session session timeout")) + + +class TestHarnessVariants: + + def test_baseline_variant_equals_production(self): + assert np.array_equal(L.VariantBM25().fit(DOCS).score(QUERY), BM25Index().fit(DOCS).score(QUERY)) + + def test_fit_tokens_equals_fit(self): + for cfg in L.VARIANTS.values(): + tok = L.DedupeTokenizer() if cfg.get("dedupe") else CodeTokenizer() + a = L.make_index(cfg).fit_tokens(DOCS, L.shared_tokens(DOCS, tok)).score(QUERY) + b = L.make_index(cfg).fit(DOCS).score(QUERY) + assert np.array_equal(a, b), cfg + + def test_dedupe_emits_each_token_once_per_word(self): + toks = L.DedupeTokenizer().tokenize_text("session refresh_token SessionManager") + assert toks == ["session", "refresh_token", "refresh", "token", "sessionmanager", "session", "manager"] + + def test_query_tf_variant_uses_query_counts(self): + idx = L.VariantBM25(query_tf=True).fit(DOCS) + assert not np.array_equal(idx.score("session timeout"), idx.score("session session session timeout")) + + def test_json_values_only_drops_keys_and_keeps_values(self): + text = L.json_leaf_text(QUERY) + assert "review_comment" not in text and "diff_hunk_context" not in text + assert "make session timeout configurable" in text and "src/auth/session.py" in text + + def test_json_values_only_leaves_non_json_unchanged(self): + assert L.json_leaf_text("plain text query") == "plain text query" + assert L.json_leaf_text("[1, 2]") == "[1, 2]" + + def test_variants_change_one_factor_each(self): + for name, cfg in L.VARIANTS.items(): + assert len(cfg) <= 1, name + + +@pytest.mark.skipif(not (PHASE3 / "protocol.json").exists(), reason="Phase 3 protocol not registered") +class TestProtocolIntegrity: + + def test_protocol_hash_matches(self): + L.verify_protocol() + + def test_dev_only_data(self): + dev, _ = L.dev_repos() + for stage in ("lexical", "hybrid"): + for p in (PHASE3 / stage).glob("shard_*.jsonl") if (PHASE3 / stage).exists() else []: + for line in open(p, encoding="utf-8"): + assert json.loads(line)["repo"] in dev + + def test_selection_artifacts_hashed(self): + for name in ("hybrid_candidates", "dev_selection"): + path = PHASE3 / f"{name}.json" + if path.exists(): + recorded = (PHASE3 / f"{name}.sha256").read_text(encoding="utf-8").split()[0] + assert L.canonical_hash(json.loads(path.read_text(encoding="utf-8"))) == recorded diff --git a/tests/test_mcp_optional_dependency.py b/tests/test_mcp_optional_dependency.py new file mode 100644 index 0000000..78861e3 --- /dev/null +++ b/tests/test_mcp_optional_dependency.py @@ -0,0 +1,32 @@ +""" +The MCP server entrypoint must fail gracefully (friendly stderr message, exit code 1) when the +optional 'mcp' extra is not installed, and the core package must remain importable. +""" + +import subprocess +import sys + +# Simulate a missing 'mcp' distribution in a fresh interpreter: a None entry in sys.modules +# makes any `import mcp...` raise ImportError. +_BLOCK_MCP = "import sys; sys.modules['mcp'] = None; " + + +def _run(code: str) -> subprocess.CompletedProcess: + return subprocess.run( + [sys.executable, "-c", _BLOCK_MCP + code], + capture_output=True, text=True, encoding="utf-8", timeout=120, + ) + + +def test_core_package_imports_without_mcp(): + proc = _run("import reponeedle, reponeedle.cli; print('ok')") + assert proc.returncode == 0, proc.stderr + assert proc.stdout.strip() == "ok" + + +def test_mcp_entrypoint_reports_missing_extra_without_traceback(): + proc = _run("from reponeedle.mcp.server import main; raise SystemExit(main(['--repo', '.']))") + assert proc.returncode == 1 + assert "pip install 'reponeedle[mcp]'" in proc.stderr + assert "Traceback" not in proc.stderr + assert proc.stdout == "" diff --git a/tests/test_mcp_server.py b/tests/test_mcp_server.py new file mode 100644 index 0000000..581705d --- /dev/null +++ b/tests/test_mcp_server.py @@ -0,0 +1,182 @@ +""" +Integration and security tests for RepoNeedle Native MCP Server. +""" + +import os +import sys +import json +import pytest +import asyncio +import tempfile +import subprocess +from pathlib import Path + +pytest.importorskip("mcp") + +from reponeedle.mcp.config import validate_and_normalize_path, MCPServerConfig +from reponeedle.mcp.server import create_mcp_server, build_parser, main + + +@pytest.fixture +def mcp_test_repo(tmp_path: Path) -> Path: + """Creates a temporary repository structure for MCP server testing.""" + repo = tmp_path / "mcp_repo" + repo.mkdir() + + src = repo / "src" + src.mkdir() + (src / "service.py").write_text( + "class Service:\n" + " def run(self) -> str:\n" + " return 'service running'\n", + encoding="utf-8", + ) + (src / "client.py").write_text( + "from src.service import Service\n\n" + "class Client:\n" + " def __init__(self):\n" + " self.svc = Service()\n", + encoding="utf-8", + ) + + tests = repo / "tests" + tests.mkdir() + (tests / "test_service.py").write_text( + "from src.service import Service\n\n" + "def test_run():\n" + " s = Service()\n" + " assert s.run() == 'service running'\n", + encoding="utf-8", + ) + return repo + + +def extract_payload(res) -> dict: + if isinstance(res, tuple): + if len(res) > 1 and isinstance(res[1], dict) and "result" in res[1]: + return res[1]["result"] + return json.loads(res[0][0].text) + if isinstance(res, list): + return json.loads(res[0].text) + return json.loads(res.text) + + +class TestMCPServerLifecycleAndSecurity: + """Test suite for server creation, tool discovery, and security boundaries.""" + + def test_parser_options(self): + parser = build_parser() + args = parser.parse_args(["--repo", "/my/path", "--budget", "8000", "--transport", "stdio", "--no-skeletons"]) + assert args.repo == "/my/path" + assert args.budget == 8000 + assert args.transport == "stdio" + assert args.no_skeletons is True + + def test_create_server_valid_repo(self, mcp_test_repo: Path): + app = create_mcp_server(repo_dir=str(mcp_test_repo), budget=4000) + assert app.name == "reponeedle" + + def test_create_server_invalid_repo_raises_value_error(self, tmp_path: Path): + fake_path = tmp_path / "non_existent_dir" + with pytest.raises(ValueError, match="does not exist or is not a directory"): + create_mcp_server(repo_dir=str(fake_path)) + + @pytest.mark.asyncio + async def test_tool_discovery(self, mcp_test_repo: Path): + app = create_mcp_server(repo_dir=str(mcp_test_repo)) + tools = await app.list_tools() + tool_names = [t.name for t in tools] + + assert "reponeedle_retrieve" in tool_names + assert "reponeedle_change_impact" in tool_names + assert "reponeedle_inspect" in tool_names + assert len(tool_names) == 3 + + def test_security_path_validation_empty_path(self, mcp_test_repo: Path): + with pytest.raises(ValueError, match="File path cannot be empty"): + validate_and_normalize_path(str(mcp_test_repo), "") + + def test_security_path_validation_non_existent_file(self, mcp_test_repo: Path): + with pytest.raises(ValueError, match="does not exist or is not a regular file"): + validate_and_normalize_path(str(mcp_test_repo), "src/phantom.py") + + def test_security_path_validation_path_traversal(self, mcp_test_repo: Path, tmp_path: Path): + outside_file = tmp_path / "secret.env" + outside_file.write_text("SECRET_KEY=123", encoding="utf-8") + + with pytest.raises(ValueError, match="resolves outside repository root"): + validate_and_normalize_path(str(mcp_test_repo), str(outside_file)) + + with pytest.raises(ValueError, match="resolves outside repository root"): + validate_and_normalize_path(str(mcp_test_repo), "../secret.env") + + def test_security_path_validation_symlink_escape(self, mcp_test_repo: Path, tmp_path: Path): + outside_file = tmp_path / "external.txt" + outside_file.write_text("External file content", encoding="utf-8") + + symlink_path = mcp_test_repo / "src" / "symlink_escape.py" + try: + os.symlink(str(outside_file), str(symlink_path)) + except (OSError, NotImplementedError): + pytest.skip("Symlink creation not permitted in this environment") + + with pytest.raises(ValueError, match="resolves outside repository root"): + validate_and_normalize_path(str(mcp_test_repo), "src/symlink_escape.py") + + @pytest.mark.asyncio + async def test_engine_reuse_across_multiple_calls(self, mcp_test_repo: Path): + """Verifies the engine is initialized once and reused across tool invocations.""" + app = create_mcp_server(repo_dir=str(mcp_test_repo)) + + # Call inspect + res_inspect = await app.call_tool("reponeedle_inspect", {}) + data_inspect = extract_payload(res_inspect) + assert data_inspect["status"] == "ready" + init_time = data_inspect["index_initialization"]["init_time_ms"] + + # Call retrieve + res_ret = await app.call_tool("reponeedle_retrieve", {"query": "run service"}) + data_ret = extract_payload(res_ret) + assert data_ret["status"] == "success" + + # Call inspect again: init_time must remain unchanged + res_inspect_2 = await app.call_tool("reponeedle_inspect", {}) + data_inspect_2 = extract_payload(res_inspect_2) + assert data_inspect_2["index_initialization"]["init_time_ms"] == init_time + + def test_stdio_protocol_integration(self, mcp_test_repo: Path): + """Tests live subprocess STDIO transport handshake and clean JSON-RPC message exchange.""" + proc = subprocess.Popen( + [sys.executable, "-m", "reponeedle.mcp", "--repo", str(mcp_test_repo)], + stdin=subprocess.PIPE, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + init_msg = json.dumps({ + "jsonrpc": "2.0", + "id": 1, + "method": "initialize", + "params": { + "protocolVersion": "2024-11-05", + "capabilities": {}, + "clientInfo": {"name": "test-client", "version": "1.0.0"}, + }, + }) + "\n" + + stdout_data, stderr_data = proc.communicate(input=init_msg, timeout=15) + assert proc.returncode in (0, None) + + # Stdout must contain ONLY valid JSON-RPC protocol line + lines = [line.strip() for line in stdout_data.strip().splitlines() if line.strip()] + assert len(lines) >= 1 + resp = json.loads(lines[0]) + + assert resp["jsonrpc"] == "2.0" + assert resp["id"] == 1 + assert "result" in resp + assert resp["result"]["serverInfo"]["name"] == "reponeedle" + + # Stderr should contain startup diagnostics, not stdout + assert "[reponeedle-mcp]" in stderr_data diff --git a/tests/test_mcp_tools.py b/tests/test_mcp_tools.py new file mode 100644 index 0000000..e232edc --- /dev/null +++ b/tests/test_mcp_tools.py @@ -0,0 +1,223 @@ +""" +Functional tests for the three RepoNeedle MCP tools. +""" + +import os +import json +import pytest +from pathlib import Path + +pytest.importorskip("mcp") +from mcp.server.fastmcp.exceptions import ToolError + +from reponeedle.mcp.server import create_mcp_server + + +@pytest.fixture +def repo_with_code(tmp_path: Path) -> Path: + """Creates a sample multi-file project for MCP tool execution tests.""" + repo = tmp_path / "sample_project" + repo.mkdir() + + src = repo / "src" + src.mkdir() + (src / "database.py").write_text( + "class DatabaseConnection:\n" + " def connect(self) -> bool:\n" + " return True\n", + encoding="utf-8", + ) + (src / "models.py").write_text( + "from src.database import DatabaseConnection\n\n" + "class UserModel:\n" + " def __init__(self):\n" + " self.db = DatabaseConnection()\n" + " def get_user(self, user_id: int) -> dict:\n" + " return {'id': user_id, 'name': 'Alice'}\n", + encoding="utf-8", + ) + (src / "helpers.py").write_text( + "def format_user_name(name: str) -> str:\n" + " return name.title()\n", + encoding="utf-8", + ) + + tests = repo / "tests" + tests.mkdir() + (tests / "test_database.py").write_text( + "from src.database import DatabaseConnection\n\n" + "def test_connection():\n" + " conn = DatabaseConnection()\n" + " assert conn.connect() is True\n", + encoding="utf-8", + ) + return repo + + +def extract_payload(res) -> dict: + if isinstance(res, tuple): + if len(res) > 1 and isinstance(res[1], dict) and "result" in res[1]: + return res[1]["result"] + return json.loads(res[0][0].text) + if isinstance(res, list): + return json.loads(res[0].text) + return json.loads(res.text) + + +class TestMCPTools: + """Test suite for reponeedle_inspect, reponeedle_retrieve, and reponeedle_change_impact.""" + + @pytest.mark.asyncio + async def test_reponeedle_inspect_tool(self, repo_with_code: Path): + app = create_mcp_server(repo_dir=str(repo_with_code)) + res = await app.call_tool("reponeedle_inspect", {}) + data = extract_payload(res) + + assert data["status"] == "ready" + assert data["indexed_file_count"] == 4 + assert len(data["indexed_path_examples"]) == 4 + assert ".py" in data["supported_file_types"] + assert "change_impact" in data["active_rankers"] + assert data["default_ranker"] == "legacy" + assert data["budget_config"]["max_tokens"] == 4000 + assert data["index_initialization"]["status"] == "initialized" + + @pytest.mark.asyncio + async def test_reponeedle_retrieve_success(self, repo_with_code: Path): + app = create_mcp_server(repo_dir=str(repo_with_code)) + res = await app.call_tool("reponeedle_retrieve", {"query": "connect database"}) + data = extract_payload(res) + + assert data["status"] == "success" + assert data["abstained"] is False + assert len(data["selected_files"]) > 0 + assert data["budget_tokens"] == 4000 + assert data["total_tokens"] <= 4000 + assert isinstance(data["rendered_context"], str) + assert len(data["rendered_context"]) > 0 + assert "src/database.py" in [sf["path"] for sf in data["selected_files"]] + + @pytest.mark.asyncio + async def test_reponeedle_retrieve_budget_enforcement(self, repo_with_code: Path): + app = create_mcp_server(repo_dir=str(repo_with_code)) + # Request a tiny budget of 150 tokens + res = await app.call_tool("reponeedle_retrieve", {"query": "connect database", "budget_tokens": 150}) + data = extract_payload(res) + + assert data["status"] == "success" + assert data["budget_tokens"] == 150 + assert data["total_tokens"] <= 150 + + @pytest.mark.asyncio + async def test_reponeedle_retrieve_different_rankers(self, repo_with_code: Path): + app = create_mcp_server(repo_dir=str(repo_with_code)) + rankers = ["legacy", "bm25", "cascade", "arm101", "change_impact"] + + for rk in rankers: + res = await app.call_tool("reponeedle_retrieve", {"query": "database connection", "ranker": rk}) + data = extract_payload(res) + assert data["status"] == "success" + assert data["ranker"] == rk + + @pytest.mark.asyncio + async def test_reponeedle_retrieve_invalid_ranker_raises_error(self, repo_with_code: Path): + app = create_mcp_server(repo_dir=str(repo_with_code)) + with pytest.raises(ToolError, match="Unknown ranker 'unknown_ranker'"): + await app.call_tool("reponeedle_retrieve", {"query": "test", "ranker": "unknown_ranker"}) + + @pytest.mark.asyncio + async def test_reponeedle_retrieve_invalid_task_type_raises_error(self, repo_with_code: Path): + app = create_mcp_server(repo_dir=str(repo_with_code)) + with pytest.raises(ToolError, match="Unknown task_type 'unknown_task'"): + await app.call_tool("reponeedle_retrieve", {"query": "test", "task_type": "unknown_task"}) + + @pytest.mark.asyncio + async def test_reponeedle_retrieve_empty_query_raises_error(self, repo_with_code: Path): + app = create_mcp_server(repo_dir=str(repo_with_code)) + with pytest.raises(ToolError, match="Query string cannot be empty"): + await app.call_tool("reponeedle_retrieve", {"query": " "}) + + @pytest.mark.asyncio + async def test_reponeedle_retrieve_with_anchor_files(self, repo_with_code: Path): + app = create_mcp_server(repo_dir=str(repo_with_code)) + res = await app.call_tool("reponeedle_retrieve", { + "query": "Fix database connection timeout", + "ranker": "change_impact", + "anchor_files": ["src/database.py"] + }) + data = extract_payload(res) + + assert data["status"] == "success" + paths = [sf["path"] for sf in data["selected_files"]] + assert "src/models.py" in paths + + @pytest.mark.asyncio + async def test_reponeedle_change_impact_single_anchor(self, repo_with_code: Path): + app = create_mcp_server(repo_dir=str(repo_with_code)) + res = await app.call_tool("reponeedle_change_impact", { + "anchor_files": ["src/database.py"], + "query": "Assess database refactor" + }) + data = extract_payload(res) + + assert data["status"] == "success" + assert data["anchor_files"] == ["src/database.py"] + assert len(data["affected_files"]) > 0 + assert isinstance(data["rendered_context"], str) + assert len(data["rendered_context"]) > 0 + + affected_paths = [af["path"] for af in data["affected_files"]] + assert "src/models.py" in affected_paths + assert "tests/test_database.py" in affected_paths + + # SFS validation + for af in data["affected_files"]: + if af["path"] == "src/database.py": + assert "Suppressed query seed" in af["explanation"] + + @pytest.mark.asyncio + async def test_reponeedle_change_impact_multiple_anchors(self, repo_with_code: Path): + app = create_mcp_server(repo_dir=str(repo_with_code)) + res = await app.call_tool("reponeedle_change_impact", { + "anchor_files": ["src/database.py", "src/models.py"], + "query": "Assess user database changes" + }) + data = extract_payload(res) + + assert data["status"] == "success" + assert len(data["anchor_files"]) == 2 + + @pytest.mark.asyncio + async def test_reponeedle_change_impact_empty_anchors_raises_error(self, repo_with_code: Path): + app = create_mcp_server(repo_dir=str(repo_with_code)) + with pytest.raises(ToolError, match="At least one anchor file is required"): + await app.call_tool("reponeedle_change_impact", {"anchor_files": []}) + + @pytest.mark.asyncio + async def test_reponeedle_change_impact_invalid_anchor_raises_error(self, repo_with_code: Path): + app = create_mcp_server(repo_dir=str(repo_with_code)) + with pytest.raises(ToolError, match="does not exist or is not a regular file"): + await app.call_tool("reponeedle_change_impact", {"anchor_files": ["src/missing.py"]}) + + @pytest.mark.asyncio + async def test_reponeedle_retrieve_abstention(self, tmp_path: Path): + """Verifies abstained result formatting when query has 0% overlap on toy repo + and the experimental abstention heuristic is explicitly enabled (opt-in since 1.2.0).""" + empty_repo = tmp_path / "toy_empty" + empty_repo.mkdir() + src = empty_repo / "src" + src.mkdir() + (src / "unrelated.py").write_text("class Unrelated:\n pass\n", encoding="utf-8") + + app = create_mcp_server(repo_dir=str(empty_repo)) + res = await app.call_tool( + "reponeedle_retrieve", + {"query": "quantum gravity entanglement", "enable_experimental_abstention": True}, + ) + data = extract_payload(res) + + assert data["status"] == "abstained" + assert data["abstained"] is True + assert data["selected_files"] == [] + assert data["rendered_context"] == "" + assert "Abstained" in data["explanation"] diff --git a/tests/test_product_aligned_eval.py b/tests/test_product_aligned_eval.py new file mode 100644 index 0000000..48c28a1 --- /dev/null +++ b/tests/test_product_aligned_eval.py @@ -0,0 +1,105 @@ +""" +Focused tests for the supplemental product-aligned evaluator +(benchmarks/run_arb_product_aligned_eval.py). Pure helpers only; the ARB-dependent check is skipped +when the external Agent Retrieval Bench checkout is not present (e.g. in clean CI). +""" + +import sys +from pathlib import Path + +import numpy as np +import pytest + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT / "benchmarks")) + +import run_arb_product_aligned_eval as E # noqa: E402 +from run_arb_evaluator_audit import reconstruct_file # noqa: E402 + +ARB_SRC = ROOT / "external" / "agent-retrieval-bench" / "src" + + +class TestProtocolHelpers: + + def test_tie_rule_score_desc_then_path_asc(self): + scored = [("b.py", 1.0), ("a.py", 1.0), ("c.py", 2.0), ("d.py", 0.0)] + assert E.rank_files(scored) == ["c.py", "a.py", "b.py", "d.py"] + + def test_chunk_to_file_rule_is_max(self): + chunks = [("a.py", 0.2), ("b.py", 0.9), ("a.py", 0.7), ("b.py", 0.1)] + assert dict(E.collapse_chunks_max(chunks)) == {"a.py": 0.7, "b.py": 0.9} + + def test_file_metrics_hand_computed(self): + ranked = [f"f{i}.py" for i in range(1, 31)] + m = E.file_metrics(["f3.py", "f12.py", "missing.py"], ranked) + assert m["mrr"] == pytest.approx(1 / 3) + assert m["recall@5"] == pytest.approx(1 / 3) + assert m["recall@10"] == pytest.approx(1 / 3) + assert m["recall@20"] == pytest.approx(2 / 3) + + def test_file_metrics_no_hit(self): + assert E.file_metrics(["x.py"], ["a.py", "b.py"]) == {"mrr": 0.0, "recall@5": 0.0, "recall@10": 0.0, "recall@20": 0.0} + + def test_unranked_gold_gets_no_position(self): + """Unselected / unranked files must never receive an artificial position.""" + assert E.gold_positions(["a.py", "z.py"], ["b.py", "a.py"]) == {"a.py": 2, "z.py": None} + + def test_bootstrap_is_deterministic_and_brackets_mean(self): + rng = np.random.default_rng(1) + diffs = list(rng.normal(0.1, 0.05, size=60)) + clusters = [f"repo{i % 6}" for i in range(60)] + a = E.clustered_bootstrap_ci(diffs, clusters, n_boot=2000) + b = E.clustered_bootstrap_ci(diffs, clusters, n_boot=2000) + assert a == b + assert a[0] <= float(np.mean(diffs)) <= a[1] + + def test_bootstrap_resamples_clusters_not_queries(self): + # One cluster: every resample is the same cluster, so the interval collapses to its mean. + ci = E.clustered_bootstrap_ci([0.1, 0.3, 0.5], ["r", "r", "r"], n_boot=500) + assert ci[0] == pytest.approx(0.3) and ci[1] == pytest.approx(0.3) + + def test_protocol_constants_fixed(self): + assert E.BOOT_SEED == 20261109 and E.BOOT_N == 10_000 + assert E.CONFIG["tie_rule"] == "score desc, path asc" + assert E.CONFIG["chunk_to_file_rule"].startswith("max") + for a, b, _ in E.COMPARISONS: + assert a in E.SYSTEMS and b in E.SYSTEMS + assert "NOT an official ARB score" in E.CONFIG["evaluation"] + + +class TestWholeFileReconstruction: + + def test_header_then_symbols_in_line_order(self): + chunks = [ + {"kind": "symbol", "start_line": 10, "text": "def b():\n return 2"}, + {"kind": "file", "start_line": 1, "text": "import os\nX = 1\n\ndef a():\n...[truncated]"}, + {"kind": "symbol", "start_line": 4, "text": "def a():\n return 1"}, + ] + text = reconstruct_file(chunks) + assert text.splitlines()[:3] == ["import os", "X = 1", ""] + assert text.index("def a()") < text.index("def b()") + assert "[truncated]" not in text + + def test_no_symbols_falls_back_to_longest_chunk(self): + assert reconstruct_file([{"kind": "file", "text": "short"}, {"kind": "file", "text": "much longer"}]) == "much longer" + + +@pytest.mark.skipif(not ARB_SRC.exists(), reason="Agent Retrieval Bench checkout not present") +class TestBM25Exactness: + + def test_precomputed_bm25_equals_arb_reference(self): + sys.path.insert(0, str(ARB_SRC)) + from agent_retrieval_bench.baseline import (rank_chunks_bm25_with_scores, tokenize, chunk_text, + BM25_K1, BM25_B) + chunks = [ + {"path": "src/session.py", "chunk_id": "1", "kind": "symbol", "symbol": "SessionManager", + "text": "class SessionManager:\n timeout = 3600\n def refresh_token(self): pass"}, + {"path": "src/auth.py", "chunk_id": "2", "kind": "file", "text": "def login(user): return token"}, + {"path": "docs/guide.md", "chunk_id": "3", "kind": "file", "text": "Session timeout configuration guide"}, + {"path": "src/auth.py", "chunk_id": "4", "kind": "symbol", "symbol": "logout", "text": "def logout(): pass"}, + ] + idx = E.PrecomputedBM25(chunks, tokenize, chunk_text, BM25_K1, BM25_B) + for q in ["session timeout refresh token", "login logout", "", "nothing matches here"]: + ref = rank_chunks_bm25_with_scores(q, chunks) + got = idx.rank(q) + assert [(s, c["chunk_id"]) for s, c in ref] == [(s, c["chunk_id"]) for s, c in got] diff --git a/tests/test_release_compat.py b/tests/test_release_compat.py new file mode 100644 index 0000000..bf77e24 --- /dev/null +++ b/tests/test_release_compat.py @@ -0,0 +1,121 @@ +""" +Release 1.2.0 compatibility guards: +- B2: change_impact reports the router-classified task for AUTO queries (published PyPI 1.1.0 + behaviour); explicit task types are unchanged; ranking is unaffected (reporting only). +- MCP serverInfo.version reports RepoNeedle's version, not the MCP SDK's. +- The `api` optional extra published with 1.1.0 is retained (optional only). +""" + +import json +import subprocess +import sys +from pathlib import Path + +import pytest + +import reponeedle +from reponeedle import RepoNeedleEngine, Query, TaskType +from reponeedle.core.router import WorkflowRouter +from reponeedle.models import FileDoc + +ROOT = Path(__file__).resolve().parent.parent +if sys.version_info >= (3, 11): + import tomllib +else: # pragma: no cover + import tomli as tomllib + +DOCS = [ + FileDoc(path="auth/session.py", content="class SessionManager:\n def refresh_token(self):\n return 'x'\n"), + FileDoc(path="auth/token.py", content="from auth.session import SessionManager\n\ndef issue():\n return SessionManager()\n"), + FileDoc(path="tests/test_session.py", content="from auth.session import SessionManager\n\ndef test_refresh():\n assert SessionManager()\n", + is_test=True), +] + + +class TestChangeImpactTaskReporting: + + @pytest.mark.parametrize("text", [ + "Find the unit tests covering SessionManager.refresh_token", + "Traceback (most recent call last):\n File \"auth/session.py\", line 3, in refresh_token\nKeyError: 'x'", + "Assess the impact of changing the session module", + ]) + def test_auto_query_reports_router_classification(self, text): + engine = RepoNeedleEngine.from_documents(DOCS) + expected, _ = WorkflowRouter().classify_intent(Query(text=text, anchor_files=["auth/session.py"])) + res = engine.retrieve(Query(text=text, anchor_files=["auth/session.py"]), ranker="change_impact") + assert res.task_type == expected + assert res.task_type != TaskType.AUTO + + @pytest.mark.parametrize("task", [TaskType.CODE2TEST, TaskType.COMMENT2CONTEXT, TaskType.EDIT2RIPPLE]) + def test_explicit_task_type_is_unchanged(self, task): + engine = RepoNeedleEngine.from_documents(DOCS) + res = engine.retrieve("assess session change", ranker="change_impact", task_type=task, anchor_files=["auth/session.py"]) + assert res.task_type == task + + def test_reporting_only_ranking_unchanged(self): + engine = RepoNeedleEngine.from_documents(DOCS) + q = "Find the unit tests covering SessionManager.refresh_token" + auto = engine.retrieve(Query(text=q, anchor_files=["auth/session.py"]), ranker="change_impact") + for task in (TaskType.CODE2TEST, TaskType.COMMENT2CONTEXT): + explicit = engine.retrieve(Query(text=q, anchor_files=["auth/session.py"], task_type=task), ranker="change_impact") + assert explicit.ranked_files == auto.ranked_files + assert [f.path for f in explicit.selected_files] == [f.path for f in auto.selected_files] + + def test_other_rankers_unaffected(self): + """The compatibility patch touches only change_impact; other rankers keep their behaviour + (an AUTO query is reported as AUTO, as in 1.1.0).""" + engine = RepoNeedleEngine.from_documents(DOCS) + for ranker in ("bm25", "arm101", "cascade"): + assert engine.retrieve("session refresh", ranker=ranker).task_type == TaskType.AUTO + assert engine.retrieve("session refresh", ranker=ranker, task_type=TaskType.CODE2TEST).task_type == TaskType.CODE2TEST + + +class TestMCPServerVersion: + + @pytest.fixture(autouse=True) + def _require_mcp(self): + pytest.importorskip("mcp") + + def test_initialization_options_report_reponeedle_version(self, tmp_path: Path): + from reponeedle.mcp.server import create_mcp_server + (tmp_path / "a.py").write_text("x = 1\n", encoding="utf-8") + app = create_mcp_server(repo_dir=str(tmp_path)) + opts = app._mcp_server.create_initialization_options() + assert opts.server_name == "reponeedle" + assert opts.server_version == reponeedle.__version__ + + def test_stdio_initialize_reports_reponeedle_version(self, tmp_path: Path): + (tmp_path / "a.py").write_text("x = 1\n", encoding="utf-8") + init = json.dumps({"jsonrpc": "2.0", "id": 1, "method": "initialize", + "params": {"protocolVersion": "2024-11-05", "capabilities": {}, + "clientInfo": {"name": "t", "version": "1"}}}) + "\n" + proc = subprocess.run([sys.executable, "-m", "reponeedle.mcp", "--repo", str(tmp_path)], input=init, + capture_output=True, text=True, encoding="utf-8", timeout=60) + resp = json.loads(proc.stdout.strip().splitlines()[0]) + assert resp["result"]["serverInfo"] == {"name": "reponeedle", "version": reponeedle.__version__} + assert resp["result"]["protocolVersion"] == "2024-11-05" # negotiated protocol unchanged + + +class TestPackageMetadata: + + def _project(self): + return tomllib.loads((ROOT / "pyproject.toml").read_text(encoding="utf-8"))["project"] + + def test_api_extra_retained_for_1_1_0_compatibility(self): + extras = self._project()["optional-dependencies"] + assert "api" in extras + names = {d.split("[")[0].split(">")[0].split("<")[0].split("=")[0].strip().lower() for d in extras["api"]} + assert names == {"fastapi", "httpx", "uvicorn"} + + def test_api_extra_dependencies_stay_optional(self): + runtime = " ".join(self._project()["dependencies"]).lower() + for name in ("fastapi", "httpx", "uvicorn", "mcp"): + assert name not in runtime + + def test_mcp_and_dev_extras(self): + extras = self._project()["optional-dependencies"] + assert any(d.startswith("mcp") for d in extras["mcp"]) + assert any(d.startswith("pytest-asyncio") for d in extras["dev"]) + + def test_version_consistency(self): + assert self._project()["version"] == reponeedle.__version__