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How to evaluate AI code reviewers

Evaluate on representative pull requests, record misses and false positives, and preserve cost and latency coverage so comparisons remain honest.

Direct answer

Evaluate on representative pull requests, record misses and false positives, and preserve cost and latency coverage so comparisons remain honest.

01Dataset design
02Adjudication
03Reproducibility

How it works

Evaluate on representative pull requests, record misses and false positives, and preserve cost and latency coverage so comparisons remain honest.

For how to evaluate ai code reviewers, use the published configuration and source repository as the product record. Keep the workflow small enough to inspect, and record any exception in the pull request rather than assuming a model result is final.

Limits to keep in view

Models, providers, and benchmark conditions change. Juror does not replace code ownership, test suites, static analysis, or a human decision to merge. Treat unknown cost and unevaluated compatibility as explicit unknowns.