What Developers Ask to ChatGPT in GitHub Pull Requests? an Exploratory Study
This study investigates how developers collaboratively integrate large language models (LLMs) into GitHub Pull Request (PR) workflows by sharing ChatGPT conversation links to facilitate code review and merging, specifically addressing: *What types of queries do developers pose to ChatGPT, and how do these influence their tangible contributions?* Method: We construct the first taxonomy of 14 query categories, grounded in empirical analysis of 155 real-world PRs containing embedded ChatGPT share links; coding and qualitative analysis are performed manually. Contribution/Results: As the first field study of human–LLM interaction within open-source development, our work systematically characterizes interaction types and collaboration patterns. Findings reveal that code review and task-oriented implementation queries dominate; code-generation requests involve significantly more conversational turns, whereas technical explanation and text polishing yield faster responses. The study elucidates the integration pathways and practical efficacy boundaries of LLMs in authentic software engineering workflows.