Xiyu Zhou

dblp:342/7552 · DBLP profile ↗
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3ranked-venue papers
1as first author
3since 2021 · last 2025
0009-0002-5946-0039ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 3 · 1 first-author · 3 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021
YearPublicationVenuePosition
2025 Exploring the problems, their causes and solutions of AI pair programming: A study on GitHub and Stack Overflow
Xiyu Zhou, Peng Liang 0001, Beiqi Zhang, Zengyang Li, Aakash Ahmad, Mojtaba Shahin, Muhammad Waseem 0011
J. Syst. Softw.1
2023 Practices and Challenges of Using GitHub Copilot: An Empirical Study
abstract
With the advances in machine learning, there is a growing interest in AI-enabled tools for autocompleting source code.GitHub Copilot, also referred to as the "AI Pair Programmer", has been trained on billions of lines of open source GitHub code, and is one of such tools that has been increasingly used since its launch in June 2021.However, little effort has been devoted to understanding the practices and challenges of using Copilot in programming with auto-completed source code.To this end, we conducted an empirical study by collecting and analyzing the data from Stack Overflow (SO) and GitHub Discussions.More specifically, we searched and manually collected 169 SO posts and 655 GitHub discussions related to the usage of Copilot.We identified the programming languages, IDEs, technologies used with Copilot, functions implemented, benefits, limitations, and challenges when 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 common used technology with Copilot is Node.js,(4) the leading function implemented by Copilot is data processing, (5) the significant benefit of using Copilot is useful code generation, and (6) the main limitation encountered by practitioners when using Copilot is difficulty of integration.Our results suggest that using Copilot is like a double-edged sword, which requires developers to carefully consider various aspects when deciding whether or not to use it.Our study provides empirically grounded foundations and basis for future research on the role of Copilot as an AI pair programmer in software development.
Beiqi Zhang, Peng Liang 0001, Xiyu Zhou, Aakash Ahmad, Muhammad Waseem 0011
SEKE3
2023 Demystifying Practices, Challenges and Expected Features of Using GitHub Copilot
abstract
With the advances in machine learning, there is a growing interest in AI-enabled tools for autocompleting source code. GitHub Copilot, also referred to as the “AI Pair Programmer”, has been trained on billions of lines of open source GitHub code, and is one of such tools that has been increasingly used since its launch in June 2021. However, little effort has been devoted to understanding the practices, challenges, and expected features of using Copilot in programming for auto-completed source code from the point of view of practitioners. To this end, we conducted an empirical study by collecting and analyzing the data from Stack Overflow (SO) and GitHub Discussions. More specifically, we searched and manually collected 303 SO posts and 927 GitHub discussions related to the usage of Copilot. We identified the programming languages, Integrated Development Environments (IDEs), technologies used with Copilot, functions implemented, benefits, limitations, and challenges when 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 common used technology with Copilot is Node.js, (4) the leading function implemented by Copilot is data processing, (5) the main purpose of users using Copilot is to help generate code, (6) the significant benefit of using Copilot is useful code generation, (7) the main limitation encountered by practitioners when using Copilot is difficulty of integration, and (8) the most common expected feature is that Copilot can be integrated with more IDEs. Our results suggest that using Copilot is like a double-edged sword, which requires developers to carefully consider various aspects when deciding whether or not to use it. Our study provides empirically grounded foundations that could inform software developers and practitioners, as well as provide a basis for future investigations on the role of Copilot as an AI pair programmer in software development.
Beiqi Zhang, Peng Liang 0001, Xiyu Zhou, Aakash Ahmad, Muhammad Waseem 0011
Int. J. Softw. Eng. Knowl. Eng.3