CodeFuse-CR-Bench: A Comprehensiveness-aware Benchmark for End-to-End Code Review Evaluation in Python Projects

• URL: http://arxiv.org/abs/2509.14856v3
• Date: Thu, 23 Oct 2025 05:44:56 GMT
• Title: CodeFuse-CR-Bench: A Comprehensiveness-aware Benchmark for End-to-End Code Review Evaluation in Python Projects
• Authors: Hanyang Guo, Xunjin Zheng, Zihan Liao, Hang Yu, Peng DI, Ziyin Zhang, Hong-Ning Dai,
• Abstract summary: We introduce CodeFuse-CR-Bench, the first comprehensiveness-aware benchmark for repository-level CR evaluation.<n>CodeFuse-CR-Bench comprises 601 high-quality instances from 70 Python projects covering nine Pull-Request (PR) problem domains.<n>We present the first large-scale assessment of state-of-the-art Large Language Models (LLMs) on this comprehensive CR task.
• Score: 23.9752442213364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated code review (CR) is a key application for Large Language Models (LLMs), but progress is hampered by a "reality gap": existing benchmarks evaluate models on isolated sub-tasks using simplified, context-poor data. This fails to reflect the holistic context-rich nature of real-world CR. To bridge this gap, we introduce CodeFuse-CR-Bench, the first comprehensiveness-aware benchmark for repository-level CR evaluation. CodeFuse-CR-Bench comprises 601 high-quality instances from 70 Python projects covering nine Pull-Request (PR) problem domains, where each instance provides rich, multi-faceted context including the associated issue, PR details, and repository state, enabling end-to-end evaluation. Beyond superficial metrics, we also propose a novel evaluation framework that combines rule-based checks for location and syntax with model-based judgments of review quality. We present the first large-scale assessment of state-of-the-art LLMs on this comprehensive CR task. Our results establish crucial baselines and reveal that (1) no single LLM dominates all aspects of CR; (2) Gemini 2.5 Pro achieves the highest comprehensive performance; and (3) different LLMs exhibit varying robustness to redundant context. These findings highlight the necessity of holistic, multi-dimensional evaluation and provide actionable insights for advancing truly intelligent yet practical CR assistants.

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