探索人工智能结对编程的问题、原因和解决方案:对 GitHub 和 Stack Overflow 的研究

IF 3.7 2区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Journal of Systems and Software Pub Date : 2024-09-02 DOI:10.1016/j.jss.2024.112204
Xiyu Zhou , Peng Liang , Beiqi Zhang , Zengyang Li , Aakash Ahmad , Mojtaba Shahin , Muhammad Waseem
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引用次数: 0

摘要

随着近年来人工智能(AI)和大型语言模型(LLM)的发展,基于 AI 的代码生成工具已成为软件开发的实用解决方案。GitHub Copilot(人工智能配对程序员)利用在大量代码片段语料库中训练的机器学习模型,通过自然语言处理生成代码建议。尽管 Copilot 在软件开发领域很受欢迎,但有关从业人员使用 Copilot 的实际经验的实证证据却很有限。为此,我们开展了一项实证研究,以了解从业人员在使用 Copilot 时所面临的问题及其根本原因和潜在解决方案。我们从 473 个 GitHub 问题、706 个 GitHub 讨论和 142 个 Stack Overflow 帖子中收集了数据。结果显示:(1) 操作问题和兼容性问题是 Copilot 用户面临的最常见问题;(2) Copilot 内部错误、网络连接错误和编辑器/IDE 兼容性问题是最常见的原因;(3) Copilot 修正的错误、修改配置/设置和使用合适的版本是最主要的解决方案。基于这些结果,我们讨论了 Copilot 的潜在改进领域,并提供了对 Copilot 用户、Copilot 团队和研究人员的启示。
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Exploring the problems, their causes and solutions of AI pair programming: A study on GitHub and Stack Overflow

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.

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来源期刊
Journal of Systems and Software
Journal of Systems and Software 工程技术-计算机:理论方法
CiteScore
8.60
自引率
5.70%
发文量
193
审稿时长
16 weeks
期刊介绍: The Journal of Systems and Software publishes papers covering all aspects of software engineering and related hardware-software-systems issues. All articles should include a validation of the idea presented, e.g. through case studies, experiments, or systematic comparisons with other approaches already in practice. Topics of interest include, but are not limited to: •Methods and tools for, and empirical studies on, software requirements, design, architecture, verification and validation, maintenance and evolution •Agile, model-driven, service-oriented, open source and global software development •Approaches for mobile, multiprocessing, real-time, distributed, cloud-based, dependable and virtualized systems •Human factors and management concerns of software development •Data management and big data issues of software systems •Metrics and evaluation, data mining of software development resources •Business and economic aspects of software development processes The journal welcomes state-of-the-art surveys and reports of practical experience for all of these topics.
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