The rise of large language models has redefined what is computationally possible in code generation, yet their potential in systematic software refactoring remains largely untapped. This article introduces RefactorGPT, a ChatGPT-augmented sequential multi-agent framework that transforms refactoring from a monolithic, opaque process into a modular, explainable, and scalable workflow. The system orchestrates four specialized agents, Analyzer, Refactor, Refine, and Fixer, to sequentially analyse source code, apply structural refactoring, enhance code quality, and recover from potential generation errors. Unlike conventional rule-based tools or one-shot large language model (LLM) prompts, RefactorGPT leverages ChatGPT iteratively across clearly defined responsibilities, enabling controlled refactoring with functional guarantees. To evaluate the framework’s effectiveness, we constructed a curated dataset encompassing nine classical refactoring techniques across three complexity levels. RefactorGPT demonstrated consistent improvements in code modularity, readability, and structural decomposition, while maintaining computational efficiency. Notably, the system achieved full execution correctness through autonomous error recovery, showcasing robustness in practical scenarios. This study contributes a reusable blueprint for LLM-integrated refactoring systems and presents a novel application of ChatGPT not merely as a code generator, but as a cooperative agent in intelligent software refactoring. The findings reveal a path forward for embedding language models into real-world developer workflows, not as assistants, but as collaborators in code evolution.
Muhammed Abdulhamid Karabıyık (2025) studied this question.