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• SWE-PRBench is a benchmark comprising 350 pull requests with human-annotated ground truth for evaluating AI code review quality.method 100%
• Eight frontier AI models detect only 15-31% of human-flagged issues on the diff-only configuration.result 100%
• AI code review performance is significantly below human expert performance.result 90%
• All 8 evaluated AI models show a monotonic degradation in performance as the context provided increases from diff-only to full context.result 100%
• The dominant reason for performance degradation with increased context is a collapse in Type2_Contextual issue detection, consistent with attention dilution.result 90%
• A structured 2,000-token diff-with-summary prompt outperforms a 2,500-token full-context prompt enriched with execution context, behavior mapping, and test signatures across all 8 models.result 100%
• The top four AI models evaluated are statistically indistinguishable in their performance, while a clear performance gap separates them from the remaining four models.result 90%
• The Repository Quality Score (RQS) used for filtering repositories includes components like review culture, PR recency, test quality, PR volume, and contamination.method 90%
• SWE-PRBench is a benchmark consisting of 350 pull requests with human-annotated ground truth for evaluating AI code review quality.method 100%
• Eight frontier AI models detect only 15-31% of human-flagged issues on the diff-only configuration.result 100%
• All 8 evaluated AI models show a monotonic degradation in performance as the context provided increases from diff-only to full context.result 100%
• The primary reason for performance degradation with increased context is a collapse in Type2_Contextual issue detection.result 90%

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