发表机构
Faculty of Engineering Kasetsart University; Faculty of Environmental, Life, Natural Science and Technology Okayama University; Faculty of Digital Technology Chitralada Technology Institute（曼谷农业大学工程学院; 冈山大学环境生命自然科学技术学部; 吉特拉达技术学院数字技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过分析VS Code代码库的25227个AI相关议题，发现开发者讨论生成式AI多聚焦于操作层面，而非调查强调的风险，且讨论正从代码补全向智能体开发演变，为相关研究提供了补充视角。
AI 中文摘要
GitHub Copilot、ChatGPT和编码智能体等生成式AI工具已迅速成为日常软件开发的一部分，但对于主流开源社区如何在实践中讨论它们，人们知之甚少。本文对2021年1月至2026年6月期间创建的43806个候选议题，在Visual Studio Code（VS Code）GitHub代码库中开展了与生成式AI相关讨论的纵向分析。为提升语料库质量，我们将关键词检索与语义相关性过滤相结合，得到了25227个与AI相关的议题构成的过滤后语料库。我们对检索到的语料库应用BERTopic来识别讨论主题，使用过滤后语料库进行主题验证和稳健性重聚类，并通过月度流行度和Mann-Kendall趋势检验分析这些主题随时间的演变。结果表明，开发者讨论主要围绕AI辅助开发环境运行的实际问题展开，包括智能体管理、配置、可靠性、身份验证和计费；而在基于调查的研究中频繁强调的风险，如幻觉和许可问题，在该平台中很少出现。这表明VS Code议题跟踪器中关于生成式AI的讨论主要聚焦于AI辅助软件开发的操作层面。此外，讨论从AI辅助代码补全向对话式和基于智能体的开发演变，反映出生成式AI日益融入软件开发工作流。这些发现表明，GitHub议题为生成式AI提供了一种实用、面向工作流的视角，可补充基于调查的开发者认知研究。
英文摘要
Generative AI tools such as GitHub Copilot, ChatGPT, and coding agents have rapidly become part of everyday software development, yet little is known about how mainstream open source communities discuss them in practice. This paper presents a longitudinal analysis of generative-AI-related discussions in the Visual Studio Code (VS Code) GitHub repository, using 43,806 candidate issues created between January 2021 and June 2026. To improve corpus quality, we combined keyword retrieval with semantic relevance filtering, yielding a filtered corpus of 25,227 AI-related issues. We applied BERTopic to the retrieved corpus to identify discussion topics, using the filtered corpus for theme validation and a robustness re-clustering, and analyzed their evolution over time using monthly prevalence and Mann-Kendall trend tests. The results show that developer discussions are dominated by practical concerns regarding the operation of AI-assisted development environments, including agent management, configuration, reliability, authentication, and billing, whereas risks frequently emphasized in survey-based studies, such as hallucination and licensing, rarely surface in this venue. This suggests that discussions of generative AI in the VS Code issue tracker primarily focus on operational aspects of AI-assisted software development. Furthermore, discussions evolved from AI-assisted code completion toward conversational and agent-based development, reflecting the increasing integration of generative AI into software development workflows. These findings suggest that GitHub Issues provide a practical, workflow-oriented perspective on generative AI that complements survey-based studies of developer perceptions.
Comments6 pages, 2 figures, 4 tables. Accepted at 34th IEEE/ACIS SNPD 2026
