{"ID":2847245,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00517","arxiv_id":"2511.00517","title":"Issue-Oriented Agent-Based Framework for Automated Review Comment Generation","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 agents are fine-tuned on curated, category-specific data to enhance task specialization. Evaluation results show that RevAgent significantly outperforms state-of-the-art PLM- and LLM-based baselines, with improvements of 12.90\\%, 10.87\\%, 6.32\\%, and 8.57\\% on BLEU, ROUGE-L, METEOR, and SBERT, respectively. It also achieves relatively higher accuracy in issue-category identification, particularly for challenging scenarios. Human evaluations further validate the practicality of RevAgent in generating accurate, readable, and context-aware review comments. Moreover, RevAgent delivers a favorable trade-off between performance and efficiency.","short_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...","url_abs":"https://arxiv.org/abs/2511.00517","url_pdf":"https://arxiv.org/pdf/2511.00517v1","authors":"[\"Shuochuan Li\",\"Dong Wang\",\"Patanamon Thongtanunam\",\"Zan Wang\",\"Jiuqiao Yu\",\"Junjie Chen\"]","published":"2025-11-01T11:44:11Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"Large Language Model\"]","has_code":false}