AI code review tools: a practical evaluation checklist

Compare tools on the same small set of known defects and clean examples. Track which findings are correct, which are missed and how much review time each alert consumes. Include the repository languages and frameworks you actually use instead of relying on a vendor-wide score.

Build a small evaluation set

1. Record one failing behavior and the expected result.
2. Add a similar clean case to expose false positives.
3. Run the checker and retain the exact version and output.
4. Review any proposed fix and rerun the regression test.

Keep adoption reversible

Start in an advisory mode while you understand the findings. Decide which checks merit blocking a merge, document exceptions and keep source access limited to what the workflow needs. See the BugClass documentation for the current detector and integration scope and the pricing page for current availability. This page makes no claim that a particular detector catches every bug in its category.