  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.
