MUARF: Leveraging Multi-Agent Workflows for Automated Code Refactoring
Refactoring is crucial for maintaining a project, but it requires developers to understand code structure and system design principles well. Recent research on Large Language Models(LLMs) has shown their great capability for handling complex tasks, making them a possible solution for overcoming thes...
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| Vydáno v: | Proceedings (IEEE/ACM International Conference on Software Engineering Companion. Online) s. 226 - 227 |
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| Jazyk: | angličtina |
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IEEE
27.04.2025
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| ISSN: | 2574-1934 |
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| Abstract | Refactoring is crucial for maintaining a project, but it requires developers to understand code structure and system design principles well. Recent research on Large Language Models(LLMs) has shown their great capability for handling complex tasks, making them a possible solution for overcoming these challenges. In this paper, we propose MUARF, an LLM-based solution designed to automate method-level code refactoring, aiming to generate correct, high-quality, and human-like refactored code. MUARF leverages Contextual Retrieval-Augmented Generation to search for similar refactoring samples for few-shot learning, uses Multi-Agent Workflow to simulate the human refactoring process, and integrates advanced software engineering tools (e.g., RefactoringMiner, PurityChecker, StyleChecker) to assist refactoring. Evaluation results show that MUARF achieves a compilation pass rate of 86.5% and a test success rate of 83.8% for the refactored code it generates. Additionally, metrics such as CodeBLEU score and AST Diff accuracy-which compare human-refactored code with the output of MUARF -highlight the generated code is human-like. The ablation results show that RefactoringMiner and Agentware made the greatest contribution to MUARF. |
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| AbstractList | Refactoring is crucial for maintaining a project, but it requires developers to understand code structure and system design principles well. Recent research on Large Language Models(LLMs) has shown their great capability for handling complex tasks, making them a possible solution for overcoming these challenges. In this paper, we propose MUARF, an LLM-based solution designed to automate method-level code refactoring, aiming to generate correct, high-quality, and human-like refactored code. MUARF leverages Contextual Retrieval-Augmented Generation to search for similar refactoring samples for few-shot learning, uses Multi-Agent Workflow to simulate the human refactoring process, and integrates advanced software engineering tools (e.g., RefactoringMiner, PurityChecker, StyleChecker) to assist refactoring. Evaluation results show that MUARF achieves a compilation pass rate of 86.5% and a test success rate of 83.8% for the refactored code it generates. Additionally, metrics such as CodeBLEU score and AST Diff accuracy-which compare human-refactored code with the output of MUARF -highlight the generated code is human-like. The ablation results show that RefactoringMiner and Agentware made the greatest contribution to MUARF. |
| Author | Xu, Yisen |
| Author_xml | – sequence: 1 givenname: Yisen surname: Xu fullname: Xu, Yisen email: yisen.xu@mail.concordia.ca organization: Gina Cody School of Engineering and Computer, Science Concordia University,Montreal,Canada |
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| Snippet | Refactoring is crucial for maintaining a project, but it requires developers to understand code structure and system design principles well. Recent research on... |
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| SubjectTerms | Code Refactoring Codes Contextual Retrieval-Augmented Generation Few shot learning Large Language Model Large language models Measurement Multi-Agent Communication Prompt engineering Retrieval augmented generation Software engineering System analysis and design |
| Title | MUARF: Leveraging Multi-Agent Workflows for Automated Code Refactoring |
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