CR-Bench: Evaluating the Real-World Utility of AI Code Review Agents
Recent advances in frontier large language models have enabled code review agents that operate in open-ended, reasoning-intensive settings. However, the lack of standardized benchmarks and granular evaluation protocols makes it difficult to assess behavior of code review agents beyond coarse success metrics, particularly for tasks where false positives are costly. To address this gap, we introduce CR-Bench, a benchmarking dataset, and CR-Evaluator, a fine-grained evaluation pipeline for code review agents. Using these tools, we conduct a preliminary study evaluating both a single-shot agent and a Reflexion-based agent across two frontier models. We find that code review agents can exhibit...
| Authors | Kristen Pereira, Neelabh Sinha, Rajat Ghosh et al. |
|---|---|
| Primary category | cs.SE |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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