Designing reliable benchmarks for AI code review tools
A practical take on what makes an AI code review benchmark trustworthy: use real-world PRs, define clear ground truth labels, measure precision/recall and noise, and ensure runs are reproducible with baselines. It frames evaluation around both detection quality and developer impact (time-to-review a...