# How to Refactor this Code? An Exploratory Study on Developer-ChatGPT Refactoring Conversations

arXiv: https://arxiv.org/abs/2402.06013 · HTML: https://mlsanity.com/paper/2402.06013/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2024.

## Built on by (most cited first, 12 of 12)

- [Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement](https://mlsanity.com/paper/2411.00622/) (2024)
- [SOEN-101: Code Generation by Emulating Software Process Models Using Large Language Model Agents](https://mlsanity.com/paper/2403.15852/) (2024)
- [Agent READMEs: An Empirical Study of Context Files for Agentic Coding](https://mlsanity.com/paper/2511.12884/) (2025)
- [Agentic Refactoring: An Empirical Study of AI Coding Agents](https://mlsanity.com/paper/2511.04824/) (2025)
- [MANTRA: Enhancing Automated Method-Level Refactoring with Contextual RAG and Multi-Agent LLM Collaboration](https://mlsanity.com/paper/2503.14340/) (2025)
- [LLMs' Reshaping of People, Processes, Products, and Society in Software Development: A Comprehensive Exploration with Early Adopters](https://mlsanity.com/paper/2503.05012/) (2025)
- [Towards Practical Defect-Focused Automated Code Review](https://mlsanity.com/paper/2505.17928/) (2025)
- [Identifying Performance-Sensitive Configurations in Software Systems through Code Analysis with LLM Agents](https://mlsanity.com/paper/2406.12806/) (2024)
- [Leveraging LLMs, IDEs, and Semantic Embeddings for Automated Move Method Refactoring](https://mlsanity.com/paper/2503.20934/) (2025)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [Automated Harmfulness Testing for Code Large Language Models](https://mlsanity.com/paper/2503.16740/) (2025)
- [Multi-Agent Coordinated Rename Refactoring](https://mlsanity.com/paper/2601.00482/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs), like ChatGPT, have gained widespread popularity and usage in various software engineering tasks, including refactoring, testing, code review, and program comprehension. Despite recent studies delving into refactoring documentation in commit messages, issues, and code review, little is known about how developers articulate their refactoring needs when interacting with ChatGPT. In this paper, our goal is to explore conversations between developers and ChatGPT related to refactoring to better understand how developers identify areas for improvement in code and how ChatGPT addresses developers' needs. Our approach relies on text mining refactoring-related conversations from 17,913 ChatGPT prompts and responses, and investigating developers' explicit refactoring intention. Our results reveal that (1) developer-ChatGPT conversations commonly involve generic and specific terms/phrases; (2) developers often make generic refactoring requests, while ChatGPT typically includes the refactoring intention; and (3) various learning settings when prompting ChatGPT in the context of refactoring. We envision that our findings contribute to a broader understanding of the collaboration between developers and AI models, in the context of code refactoring, with implications for model improvement, tool development, and best practices in software engineering.