An AI review false positive can arise from an inferred dependency, misunderstood requirement or missing repository context. It consumes reviewer attention and may trigger unnecessary code changes.
Teams should track rejected findings and feed representative cases into evaluation. Explanations and severity labels should remain editable so domain experts can correct the system without hiding disagreement.
ELI5
A review false positive happens when an automated reviewer reports a problem that is not actually a problem in the code's real context. It may misunderstand a requirement, miss a dependency, or apply a general rule where a justified exception exists.
For example, AI might warn that a function is unused even though a framework calls it by name at runtime. Reviewers should reject the finding with a reason, and teams can add similar cases to evaluations so the tool improves without hiding disagreement.
What causes false positives in AI code review?
Common causes include missing context, inaccurate dependency inference, ambiguous rules and a model generalizing from unrelated patterns.
How can teams reduce review false positives?
They can improve context, calibrate severity, evaluate rejected findings and move reliable rules into deterministic checks.
