CodeQL benchmark runner + blog post draft#19
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…adation Implements CodeQLRunner following the same ToolRunner interface as SemgrepRunner: shells out to the codeql CLI, creates a per-sample database in a temp directory, runs the appropriate language query pack, and parses SARIF output into normalized ToolFinding objects. Key design decisions: - is_available() returns False when codeql is not in PATH — benchmark runs cleanly without it installed - Rust is skipped (None pack) because CodeQL has no Rust extractor yet - SARIF severity is resolved from result properties first, then rule metadata, then the top-level "level" field - Uses a temporary source directory (database create requires a dir, not a single file) and cleans up via TemporaryDirectory context Wired into harness._build_runners alongside OdinRulesRunner and SemgrepRunner so it appears in the leaderboard when available.
Publication-ready draft (~1450 words) covering the benchmark methodology, why false positive rate matters as much as recall, the two-stage dataflow triage architecture, the feedback learning loop, and an honest limitations section. Includes the actual benchmark numbers and a reproducible one-liner. Structured for HN front page: senior-engineer tone, results including where Odin loses, no marketing language.
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Pull request overview
Adds a CodeQL-backed tool runner to the benchmark harness and introduces a draft benchmark write-up intended for publication.
Changes:
- Implement
CodeQLRunnerto runcodeql database create/analyzeper sample and normalize SARIF results intoToolFindings. - Register CodeQL as a candidate runner in
bench/harness.pyso it appears when the CodeQL CLI is installed. - Add
BLOG_POST.mddescribing benchmark methodology and current results.
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 4 comments.
| File | Description |
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| BLOG_POST.md | Draft blog post describing the benchmark methodology/results and how to reproduce them. |
| backend/bench/tools/codeql.py | New benchmark runner that shells out to CodeQL CLI and parses SARIF into normalized findings. |
| backend/bench/harness.py | Wires CodeQLRunner into the benchmark runner list. |
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| # I benchmarked every AI code reviewer on 500 real CVEs. Here's what I found. | ||
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| Most of them are generating noise at a rate that would get a human reviewer fired. | ||
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| I built a reproducible benchmark to measure two things: how often these tools flag clean, production-quality code as buggy (false positive rate), and how often they actually catch real vulnerabilities (recall). The headline result: the tools with the highest recall also tend to have the worst false positive rates. The signal-to-noise tradeoff is not a footnote — it is the central problem in this space, and almost nobody is measuring it honestly. |
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The headline claims this benchmark covers "500 real CVEs", but the benchmark datasets in this repo currently use a 60-snippet clean corpus and a 14-sample SecVulEval subset (see backend/bench/datasets/secvuleval.py). This is a factual mismatch that will confuse readers and undermines reproducibility; please update the headline (and any other references) to match the actual dataset size/source, or expand the dataset to match the claim.
| codeql_lang = _LANG_TO_CODEQL_LANG.get(sample.language) | ||
| query_pack = _LANG_TO_PACK.get(sample.language) | ||
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| # Gracefully skip unsupported languages (e.g. Rust) | ||
| if codeql_lang is None or query_pack is None: | ||
| return [], 0.0 | ||
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Returning an empty findings list for unsupported languages (e.g. Rust) makes the harness score those samples as true negatives on the clean corpus, which artificially improves CodeQL’s FP-rate despite the tool not analyzing the code. If the intent is to “skip” unsupported languages, the harness needs a notion of “skipped sample” (excluded from FP/recall metrics) or the runner should be filtered to only run on supported languages before classification.
| ## Results | ||
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| | Tool | FP Rate (clean corpus) | Recall (CVEs) | F1 | | ||
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| | odin-rules | 0.0% | 86% | 0.92 | | ||
| | semgrep (auto) | 8.3% | 71% | 0.80 | | ||
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The Markdown table in the Results section uses || at the start of each row (e.g. || Tool | ...), which renders as an extra empty column or may not render as a table depending on the renderer. Replace the double leading pipes with a single | for each table row so the table renders correctly.
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| *The benchmark runs on every commit. The leaderboard is at `backend/bench/reports/leaderboard.md`. Show HN: I built a reproducible benchmark that measures false positive rate alongside recall, because a tool that cries wolf is worse than no tool at all.* |
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The post says “The benchmark runs on every commit” and points to backend/bench/reports/leaderboard.md, but the current CI workflow runs backend/eval/runner.py --rules-only and does not run python -m bench.harness or regenerate the leaderboard. Either update the statement to reflect what actually runs in CI, or add a CI step to run the bench harness and commit/publish the regenerated leaderboard.
| *The benchmark runs on every commit. The leaderboard is at `backend/bench/reports/leaderboard.md`. Show HN: I built a reproducible benchmark that measures false positive rate alongside recall, because a tool that cries wolf is worse than no tool at all.* | |
| *The benchmark leaderboard is at `backend/bench/reports/leaderboard.md`. Show HN: I built a reproducible benchmark that measures false positive rate alongside recall, because a tool that cries wolf is worse than no tool at all.* |
Summary
backend/bench/tools/codeql.py— aToolRunnerimplementation that invokes thecodeqlCLI, creates a per-sample database in a temp directory, runs the appropriate language query pack, and parses SARIF output into normalizedToolFindingobjectsCodeQLRunnerintobench/harness.pyalongsideOdinRulesRunnerandSemgrepRunner; it appears in the leaderboard when the CLI is installed and is silently skipped when it is notBLOG_POST.md(~1450 words) — a publication-ready benchmark write-up covering FP rate methodology, the dataflow triage architecture, the learning loop, and honest numbers including where Odin losesCodeQL runner design
is_available()returnsFalsewhencodeqlis not in PATH — graceful degradation, no benchmark failuresNone) because CodeQL has no Rust extractorproperties["problem.severity"]first, then rule metadata, then top-level"level"fieldjavascriptextractorTest plan
python -m bench.harness --tool codeqlexits cleanly whencodeqlis not installed (returns no results, no crash)codeqlinstalled:python -m bench.harnessruns all three tools and includescodeqlin the leaderboard output_parse_sarifcorrectly mapserror→ CRITICAL,warning→ HIGH,note/recommendation→ MEDIUM