What is an artificial intelligence review false positive?
Definition
An artificial intelligence 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.
Acronyms and aliases
AI review false positive acronym
Related terms
Frequently asked questions
What causes false positives in artificial intelligence 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.
