AI code review: what should we actually measure?
Evaluating AI-assisted pull-request review needs measurable quality signals, not only subjective productivity claims.
Most AI review discussions stop at speed, but quality is a richer question.
I am evaluating a review workflow using dimensions such as correctness, security, architecture and maintainability, alongside false positives and review effort.
The experiment is still running, and the key question is not whether AI comments are numerous, but whether they improve decisions without increasing review noise.
Dimensions being evaluated
- correctness
- security
- architecture
- maintainability
- false positives
- human findings missed by AI
- AI findings missed by humans
- review effort
AI engineering
code review
quality