AI Code Reviewers in CI Pipelines Need Their Own Test Suites, Experts Warn

AI-powered code reviewers integrated into pull-request pipelines can silently degrade over time when prompts are tweaked or model versions are updated, often without anyone noticing. Unlike the code they evaluate, these AI reviewers typically have no test coverage of their own, leaving teams unaware of regressions in bug-detection behavior. A structured testing approach involves collecting real or synthetic pull requests with known, labeled defects and running them through the reviewer to assert whether specific findings are correctly flagged or appropriately omitted. Using tools like Python, pytest, and structured JSON output, developers can define fixtures that pin both precision and recall, checking that a reviewer catches genuine issues without flooding teams with irrelevant noise. The core argument is that an AI system making judgment calls on every commit in an organization warrants the same quality assurance discipline applied to any other production software component.