Issue-Oriented Agent-Based Framework for Automated Review Comment Generation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912683136122880 |
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| author | Li, Shuochuan Wang, Dong Thongtanunam, Patanamon Wang, Zan Yu, Jiuqiao Chen, Junjie |
| author_facet | Li, Shuochuan Wang, Dong Thongtanunam, Patanamon Wang, Zan Yu, Jiuqiao Chen, Junjie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00517 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Issue-Oriented Agent-Based Framework for Automated Review Comment Generation Li, Shuochuan Wang, Dong Thongtanunam, Patanamon Wang, Zan Yu, Jiuqiao Chen, Junjie Software Engineering 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. |
| title | Issue-Oriented Agent-Based Framework for Automated Review Comment Generation |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2511.00517 |