  With the recent advancement of Artificial Intelligence (AI) and Large
Language Models (LLMs), AI-based code generation tools become a practical
solution for software development. GitHub Copilot, the AI pair programmer,
utilizes machine learning models trained on a large corpus of code snippets to
generate code suggestions using natural language processing. Despite its
popularity in software development, there is limited empirical evidence on the
actual experiences of practitioners who work with Copilot. To this end, we
conducted an empirical study to understand the problems that practitioners face
when using Copilot, as well as their underlying causes and potential solutions.
We collected data from 473 GitHub issues, 706 GitHub discussions, and 142 Stack
Overflow posts. Our results reveal that (1) Operation Issue and Compatibility
Issue are the most common problems faced by Copilot users, (2) Copilot Internal
Error, Network Connection Error, and Editor/IDE Compatibility Issue are
identified as the most frequent causes, and (3) Bug Fixed by Copilot, Modify
Configuration/Setting, and Use Suitable Version are the predominant solutions.
Based on the results, we discuss the potential areas of Copilot for
enhancement, and provide the implications for the Copilot users, the Copilot
team, and researchers.
