@inproceedings{wang-etal-2026-comments,
    title = "Let the Comments Speak: A Multi-Agent Framework based on Large Language Model for Comment-Guided Code Refactoring",
    author = "Wang, Zixuan  and
      Yu, Wutong  and
      Zhou, Deyu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1620/",
    pages = "32377--32393",
    ISBN = "979-8-89176-395-1",
    abstract = "Code refactoring is essential for software maintainability, yet current Large Language Model (LLM) based frameworks primarily focus on syntax and neglect the vital semantic signals in code comments. As pointed out in Fowler{'}s refactoring theory, explanatory comments function as semantic anchors that provide necessary guidance for method extraction. Moreover, research reports show that more than 84 percent of codebases lack appropriate code comments, which hinders the leverage of such guidance. To bridge this gap, we propose MACOR, a Multi-Agent framework for COmment-guided code Refactoring. In specific, MACOR populates original code with precise comments to provide necessary semantic guidance for the subsequent refactoring process. These generated signals are employed to retrieve expert examples. An iterative feedback is incorporated loop for validation. Experiments are conducted on three benchmarks using three base LLMs. Experimental results show that MACOR significantly optimizes code quality and achieves higher developer acceptance compared to the representative baselines."
}