2025-03-19 ยท Soojin Lee
Code Review Automation Should Start With Comment Quality
Code review automation can create noise quickly. A team should define comment quality before enabling AI-assisted review in active repositories. Good comments are specific, actionable, and tied to the team standard. Weak comments repeat generic advice or create work that a human reviewer must clean up.
Start with a review taxonomy. Separate correctness, security, maintainability, testing, performance, and documentation concerns. Then decide which categories are suitable for AI suggestions and which require human discussion from the beginning.
False positives deserve explicit tracking. If engineers spend more time dismissing comments than acting on them, the workflow will lose trust. A short pilot with labeled comment outcomes can reveal where automation is helping and where it is simply louder.
The healthiest pattern is assisted review, not delegated review. AI can prepare observations and surface patterns, but the human reviewer owns the judgment and the tone of the final feedback.