Issue-Oriented Agent-Based Framework for Automated Review Comment Generation
Shuochuan Li, Dong Wang, Patanamon Thongtanunam, Zan Wang, Jiuqiao Yu, Junjie Chen

TL;DR
RevAgent is an innovative agent-based framework that improves automated code review comment generation by decomposing the task into specialized stages, significantly outperforming existing models in accuracy and efficiency.
Contribution
The paper introduces RevAgent, a novel multi-stage, issue-oriented framework that enhances comment relevance and accuracy in automated code reviews through specialized agents and fine-tuning.
Findings
RevAgent outperforms state-of-the-art baselines in BLEU, ROUGE-L, METEOR, and SBERT metrics.
It achieves higher accuracy in issue-category identification, especially in complex scenarios.
Human evaluation confirms RevAgent's effectiveness in generating practical, context-aware review comments.
Abstract
Code review (CR) is a crucial practice for ensuring software quality. Various automated review comment generation techniques have been proposed to streamline the labor-intensive process. However, existing approaches heavily rely on a single model to identify various issues within the code, limiting the model's ability to handle the diverse, issue-specific nature of code changes and leading to non-informative comments, especially in complex scenarios such as bug fixes. To address these limitations, we propose RevAgent, a novel agent-based issue-oriented framework, decomposes the task into three stages: (1) Generation Stage, where five category-specific commentator agents analyze code changes from distinct issue perspectives and generate candidate comments; (2) Discrimination Stage, where a critic agent selects the most appropriate issue-comment pair; and (3) Training Stage, where all…
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Taxonomy
TopicsSoftware Engineering Research · Topic Modeling · Software Engineering Techniques and Practices
