Evaluating code review agents with ReviewBench

ReviewBench is an internally developed benchmark designed to evaluate code review agents by measuring their effectiveness in identifying issues found in real pull requests (PRs) from the LangSmith mono-repo. Unlike synthetic benchmarks, ReviewBench is built on curated comments from trusted reviewers, focusing on concrete, verifiable issues tied to specific codebase standards. The process involves converting raw review comments into tasks using the Harbor format, enabling agents to assess PR changes comprehensively rather than merely scanning for superficial bugs. The benchmark scores agents on coverage and precision, with results indicating that most agents, even with a basic harness, miss many nuanced issues that human reviewers typically catch. However, the study highlights that altering review strategies, such as using structured prompts, can significantly enhance an agent's performance by encouraging a more thorough analysis of code changes. The initiative aims to expand ReviewBench's scope to include a broader array of review tasks, ultimately helping code review agents detect substantive issues without generating excessive review noise.

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