SWE-PRBench: Benchmarking AI Code Review Quality Against Pull Request Feedback

• URL: http://arxiv.org/abs/2603.26130v1
• Date: Fri, 27 Mar 2026 07:24:26 GMT
• Title: SWE-PRBench: Benchmarking AI Code Review Quality Against Pull Request Feedback
• Abstract summary: SWE-PRBench is a benchmark of 350 pull requests with human-annotated ground truth for evaluating AI code review quality.<n>Eight frontier models detect only 15-31% of human-flagged issues on the diff-only configuration.
• Score: 2.626039639665796
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce SWE-PRBench, a benchmark of 350 pull requests with human-annotated ground truth for evaluating AI code review quality. Evaluated against an LLM-as-judge framework validated at kappa=0.75, 8 frontier models detect only 15-31% of human-flagged issues on the diff-only configuration, demonstrating that AI code review remains far below human expert performance despite strong results on code generation benchmarks. Pull requests are drawn from active open-source repositories, filtered from 700 candidates using a Repository Quality Score, and evaluated under three frozen context configurations: diff only (config_A), diff with file content (config_B), and full context (config_C), enabling systematic ablation of context provision strategies. All 8 models degrade monotonically from config_A to config_C, even when context is provided via structured semantic layers including AST-extracted function context and import graph resolution. The dominant mechanism is a collapse of Type2_Contextual issue detection at config_B, consistent with attention dilution in long contexts: a structured 2,000-token diff-with-summary prompt outperforms a 2,500-token full-context prompt enriched with execution context, behaviour mapping, and test signatures across all 8 models. The top four models are statistically indistinguishable (mean score 0.147-0.153) while a clear tier gap separates them from the remaining four (mean score <= 0.113). Dataset, contexts, annotations, and evaluation harness are released publicly.

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