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Precision, recall, and catch rate are not interchangeable. A benchmark can show how a tool performed under one evaluation; it cannot, by itself, establish how well that tool will work on your codebase or fit your team’s review process.
What a code review benchmark score actually measures
A score describes performance on a particular test, not an abstract capability. Its meaning depends on which pull requests were selected, what code and repository context the tool could see, how expected findings were labeled, which tool versions and settings were used, and how outputs were scored.
Those choices can change the result. A set built around bug-fix pull requests may test whether a tool catches known defects, while a broader review benchmark may also score false positives, severity, and issue category. A diff-only evaluation may not test the same capability as one that gives the tool full-repository context. Even two studies that use the same metric name may define valid findings differently.
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GitHub’s ReviewBench post, published October 5, 2026, puts the design goal this way: “A good code review benchmark should reflect the diversity of real pull requests, capture a broad set of review findings, and support meaningful breakdowns by severity, category, and precision-recall preferences.” That is a useful standard for assessing benchmark design, not evidence that any one ranking is definitive.
Read the metric before reading the ranking
For review findings, a true positive is a surfaced comment judged to identify a valid issue; a false positive is an invalid or unhelpful finding under the benchmark’s rules; and a false negative is a valid expected issue the tool missed. The exact judgment rules matter, so check how the benchmark defines each category.
- Precision is the share of surfaced findings that are valid. Low precision means more reviewer time spent sorting through bad or irrelevant comments.
- Recall is the share of known valid findings the tool found. Low recall means more expected issues were missed.
- F1 combines precision and recall with equal weighting. It is useful only if that balance reflects the team’s needs.
- F-beta weights one side more heavily than the other. The report should state the beta value and explain why that weighting fits its intended use.
- Catch rate may mean only the proportion of planted or known defects detected. Unless the benchmark also evaluates invalid findings and defines its denominator, do not read it as precision or as a complete measure of review quality.
A benchmark that counts only one known bug per pull request can measure detection of that bug while overlooking other valid findings and false positives. Reference comments are not automatically a complete list of everything worth flagging. Ask how labels were created, whether reviewers looked for additional issues, and whether disagreements were adjudicated.
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What prominent benchmark designs establish
The examples below test different things. Their scores should not be combined into a single cross-benchmark leaderboard.
The Tool Desk
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|---|---|---|
| ReviewBench GitHub, announced October 2026 |
GitHub describes a corpus of 219 public pull requests across 19 languages, modeled on distributions from 103.9 million GitHub pull requests. Its golden set draws on human reviewers, frontier LLMs, and static analysis; findings have severity and category labels. GitHub says senior engineers independently labeled golden true positives, with 96.6% agreement in that check. The research preview includes the dataset, labels, methodology, judge prompt, configuration, runner, and leaderboard. | Among the examples here, it has the broadest published dataset description. GitHub says it uses the benchmark to help anticipate production experiments for Copilot Code Review. That purpose and its publisher should be considered when interpreting the results: benchmark validation is not an independent ranking of all tools. |
| Code Review Bench Martian open-source project; repository page accessed October 2026 |
The fixed offline set has 50 pull requests from five major open-source projects and 173 human-verified golden comments. A separate online set samples recently merged pull requests that received review-bot comments. The project publishes data, judge prompts, and pipeline code. It reports using three judge models in its described offline evaluation and says top-five membership remained the same across those judges. | The offline set supports repeatable runs, while a continuously refreshed online set is intended to reduce the chance that tools memorized those exact cases. Martian also acknowledges training-leakage and LLM-judge variability risks. Its reported judge agreement result is a project report, not proof that every ranking is free of evaluator effects. |
| Greptile’s evaluation Greptile, July 2025 |
Ten real bug-fix pull requests from each of five repositories: Sentry, Cal.com, Grafana, Keycloak, and Discourse. Tools ran on hosted plans with default settings and repository and pull-request context. A bug counted as caught only when a tool identified the faulty code in a line-level comment and explained the impact. False positives, style suggestions, and unrelated comments did not affect the reported catch rate. | This is a vendor-published test of a narrow detection task. It does not report the precision-versus-recall trade-off for the full review experience, and its percentages should not be compared directly with precision or recall from other datasets. |
| SWRBench Research paper, 2025 abstract |
The authors describe 1,000 manually verified GitHub pull requests with full project context. An LLM-based evaluator checks whether generated reviews cover structured ground-truth issues. The abstract reports approximately 90% agreement with human judgment. | The reported agreement concerns the evaluator’s judgments; it does not mean tools achieved 90% review accuracy. The page also includes later journal metadata, which is distinct from the date of the benchmark report in the abstract. |
| AI Code Review Evaluations Repository comparison, 2025 |
The repository says the original Greptile set had one golden comment per pull request, even though additional valid findings may exist. Its authors manually reviewed pull requests and tool findings to expand expected comments, then used an LLM to match comments by underlying issue rather than exact wording or line number. It evaluated seven tools against the expanded set and excluded low-severity comments from its main scoring treatment. | This illustrates how label completeness, issue matching, and severity exclusions can alter a comparison. A larger expected-comment set may recognize findings a one-comment-per-PR reference misses, but its own review and scoring rules still need to be examined. |
In Greptile’s July 2025 test, Greptile reported an 82% catch rate, Cursor Bugbot 58%, GitHub Copilot 54%, CodeRabbit 44%, and Graphite 6%. These are publisher-reported results for the 50-pull-request bug-fix evaluation described above—not a general ranking, and not precision or recall figures.
Why security needs its own breakdown
A general review score can hide weak performance on the defects your team most needs to catch. Injection, authorization, and business-logic flaws require different evidence and context; performance on obvious injection cases does not establish how well a tool reasons about request permissions or application-specific rules.
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Safeguard reported a two-week field evaluation conducted in August 2025 and published in June 2026. It tested five review systems on 240 seeded defects in TypeScript, Python, and Go. The organization reported an average hallucination rate of 18%, no tool exceeding 70% recall on injection-class bugs, and weaker results on authorization flaws requiring request context. Its reported recall figures were CodeRabbit 64%, Claude Sonnet 4.5 baseline 61%, Copilot Code Review 54%, Qodo Merge 49%, and CodeGuru 41%.
Those results describe Safeguard’s seeded-defect test, not expected rates across arbitrary repositories or current tool versions. For a security evaluation, include categories relevant to your application—especially authorization and business-logic cases—and report both detected defects and false findings.
Use a controlled local evaluation
A team’s own comparison should make the task explicit before tools run. The following protocol makes results easier to reproduce and more relevant to deployment decisions.
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- Define a useful finding. Specify issue categories, severity thresholds, and whether style-only suggestions count. Decide how to treat duplicates, comments outside changed lines, and findings that are valid but not actionable.
- Select representative pull requests. Sample across the languages, repository sizes, change shapes, and risk areas your team actually handles. Use the same pull requests and repository context for every tool. If you want to test full-repository reasoning, provide that context consistently rather than mixing it with diff-only runs.
- Freeze and record tool configuration. Record each tool’s version, plan, model or configuration where disclosed, prompts or rules, and whether settings are default or customized. Repeat runs when outputs vary, and preserve the outputs so the comparison can be audited.
- Build and review the reference findings. Have qualified reviewers identify expected issues in each pull request, include multiple valid findings where they exist, label severity and category, and adjudicate disagreements. Do not assume that one existing comment exhausts a pull request’s valid findings.
- Match comments by issue, then count outcomes. Match a tool comment to an expected finding by the underlying issue, not exact wording or line number. Record true positives, false positives, and false negatives. Report precision and recall separately; add F1 or F-beta only with its weighting stated.
- Break out the results that matter. Show performance by severity and category, especially for security or reliability priorities. Also record comment volume and time-to-comment so the team can assess review burden and operational fit.
- Check whether offline gains transfer. Repeat the evaluation on fresh pull requests or run a controlled live pilot. Compare benchmark outcomes with production experience before treating a small offline lead as a reason to standardize.
Account for leakage, judge variability, and test validity
Public fixed datasets make repeatable comparison possible, but the cases may become familiar to model developers or appear in training data. Martian’s pairing of a fixed offline set with a stream of recently merged pull requests is one design response; it does not eliminate every contamination risk. Record dataset publication and refresh dates, and use fresh or private examples when you can do so without compromising code confidentiality.
LLM judges can vary, so a result that depends on one evaluator deserves scrutiny. Check whether the benchmark publishes its judge prompt and whether results are tested across judges or reviewed by humans. Human validation can improve confidence in labels, but it does not make the dataset representative of every engineering team.
Test design matters beyond code review. OpenAI’s 2026 analysis of SWE-bench Verified—a code-solving benchmark, not an AI code review evaluation—reported that at least 59.4% of the 138 audited problems had material test-design or problem-description issues, including tests that rejected functionally correct submissions. OpenAI also reported evidence that tested frontier models could reproduce original patches or problem details from training exposure. The relevant lesson is to audit both reference correctness and contamination exposure; a code-generation benchmark score is not a proxy for review quality.
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How to use the result in a decision
Use public benchmarks to understand what a tool has demonstrated under documented conditions and to identify questions for your own evaluation. Prefer comparisons that disclose the corpus, repository context, versions and settings, reference-label process, judge, metric definitions, severity and category breakdowns, and false-positive treatment.
Then compare shortlisted tools under the same local conditions. A small change in an offline score may matter less than whether the tool catches high-severity issues, avoids noisy comments, works across your stack, respects deployment and privacy requirements, integrates with your review workflow, and comments quickly enough to be useful. No benchmark percentage settles those trade-offs for a team whose repositories and review standards differ from the test set.
Quick Recap
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