From Static to Dynamic: Benchmarking Real-World Code Review with MCR-Bench
Provides critical insights into LLM performance gaps for realistic software engineering workflows requiring multi-round interactions.
AI Summary
Researchers introduce MCR-Bench, the first defect state-aware for multi-round code review, evaluating LLMs' limitations in iterative defect detection and tracking.
Excerpt
In real-world software development, code review typically involves iterative interactions between developers and reviewers to improve software quality, making the process costly and time-consuming. Although recent work explores large language models (LLMs) for automated code review, most approaches oversimplify code review into a single-round, static decision task, which fails to capture the multi-round interactive nature and the complex problem-solving processes inherent in realistic review sce