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With the advances in machine learning, there is a growing interest inAI-enabled tools for autocompleting source code. GitHub Copilot has beentrained on billions of lines of open source GitHub code, and is one of suchtools that has been increasingly used since its launch in June 2021. However,little effort has been devoted to understanding the practices, challenges, andexpected features of using Copilot in programming for auto-completed sourcecode from the point of view of practitioners. To this end, we conducted anempirical study by collecting and analyzing the data from Stack Overflow (SO)and GitHub Discussions. We searched and manually collected 303 SO posts and 927GitHub discussions related to the usage of Copilot. We identified theprogramming languages, Integrated Development Environments (IDEs), technologiesused with Copilot, functions implemented, benefits, limitations, and challengeswhen using Copilot. The results show that when practitioners use Copilot: (1)The major programming languages used with Copilot are JavaScript and Python,(2) the main IDE used with Copilot is Visual Studio Code, (3) the most commonused technology with Copilot is Node.js, (4) the leading function implementedby Copilot is data processing, (5) the main purpose of users using Copilot isto help generate code, (6) the significant benefit of using Copilot is usefulcode generation, (7) the main limitation encountered by practitioners whenusing Copilot is difficulty of integration, and (8) the most common expectedfeature is that Copilot can be integrated with more IDEs. Our results suggestthat using Copilot is like a double-edged sword, which requires developers tocarefully consider various aspects when deciding whether or not to use it. Ourstudy provides empirically grounded foundations that could inform developersand practitioners, as well as provide a basis for future investigations.

Demystifying Practices, Challenges and Expected Features of Using GitHub Copilot