On the Concerns of Developers When Using GitHub Copilot: Conclusion & References

Written by textmodels | Published 2024/03/04
Tech Story Tags: github-copilot | ai-code | ai-code-generation | software-development | can-ai-code | github-copilot-issues | ai-applications | github-copilot-user-experience

TLDRThis conclusion summarizes key findings from the study on GitHub Copilot, including common user issues, causes, and solutions identified through data collected from GitHub Issues, Discussions, and Stack Overflow. Recommendations include enhancing compatibility, customization options, code quality, and addressing concerns about intellectual property. Future plans involve user surveys and code testing experiments to evaluate Copilot's usage and performance.via the TL;DR App

Authors:

(1) Xiyu Zhou, School of Computer Science, Wuhan University, Wuhan, China;

(2) Peng Liang, School of Computer Science, Wuhan University, Wuhan, China;

(3) Zengyang Li, School of Computer Science, Central China Normal University, Wuhan, China;

(4) Aakash Ahmad, School of Computing and Communications, Lancaster University Leipzig, Leipzig, Germany;

(4) Mojtaba Shahin, School of Computing Technologies, RMIT University, Melbourne, Australia;

(4) Muhammad Waseem, Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland.

VII. CONCLUSIONS

In this study, we focused on the issues users may encounter when using GitHub Copilot, as well as their underlying causes and potential solutions. Following identifying the RQs, we collected data from GitHub Issues, GitHub Discussions and SO posts. After manual screening, we obtained 476 GitHub Issues, 706 GitHub Discussions, and 184 SO posts related to Copilot and got a total of 1399 issues, 337 causes, and 497 solutions based on our data extraction criteria. The results indicate that Usage Issue and Compatibility Issue are the most common problems faced by users, Copilot Internal Issue, Network Connection Issue, and Editor/IDE Compatibility Issue are identified as the most usual causes of issues, and Bug Fixed by Copilot, Modify Configuration/Setting and Use Suitable Version are the predominant solution. Our findings suggest that Copilot should enhance compatibility across various IDEs and editors, simplify the configuration, improve the quality of generated code, and address concerns related to intellectual property and copyright. Additionally, users require more customization options to tailor Copilot’s behavior and have more control over the content generated by Copilot. In light of the additional time required for code suggestion verification when utilizing Copilot, the integration of a code explanation feature becomes imperative to enhance its overall utility and effectiveness in practical development scenarios.

In the next step, we plan to combine a survey and code testing experiments to evaluate the actual usage of Copilot by users, as well as its performance in terms of security, maintainability, and other aspects.

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This paper is available on arxiv under CC 4.0 license.


Written by textmodels | We publish the best academic papers on rule-based techniques, LLMs, & the generation of text that resembles human text.
Published by HackerNoon on 2024/03/04