GitHub introduced ReviewBench on October 5, a public benchmark for evaluating AI code review. Its evaluation separates valid findings from missed issues and lets readers inspect results by severity and category.
That distinction is useful for a small team too. Ten comments about naming and one reproducible authorization bug are very different outcomes.
Try a small evaluation on changes you already understand. Keep the model and instructions fixed. For each finding, record whether you can reproduce it, whether you changed code because of it, and how serious the defect was. Also record the important issues the reviewer missed.
Use an ordinary patch as well as a deliberately faulty one. A reviewer that constantly invents problems creates work even when its comments sound convincing.
If you ask the system for “more thorough” reviews, check whether it finds more real defects or merely writes longer explanations. Separate style preferences from bugs before calculating your result.
My takeaway: decide which decisions you want assistance with, then measure those decisions. Treat a leaderboard as a starting point for evaluation on your codebase.
The cover is an editorial illustration, not a measured finding from this benchmark.
Disclosure: This article was prepared with AI assistance and reviewed against the linked sources and implementation. Examples explicitly marked fictional are illustrative.
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