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DIY Method to Evaluate AI Code Review Tools Beyond Vendor Benchmarks

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A software developer has outlined a practical, self-run evaluation method for AI code review tools after finding vendor-published benchmarks unreliable on real repositories. The approach involves running a candidate tool against 20 previously merged pull requests and classifying every flag it raises as real, noise, or actively wrong advice. Two key metrics guide the assessment: the bad-advice rate, which flags dangerous recommendations, and the triage burden, which measures how much time engineers waste sorting unhelpful output. The developer found that most tool failures stemmed from misconfiguration or missing context rather than underlying model quality, and that default settings are typically optimised for demos rather than real team workflows. Running the evaluation twice — once with defaults and once tuned to the team's actual CI setup — is recommended for meaningful results.

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