SearcharxivSearch

arXiv · 2609.04680

How Developers Discuss Generative AI: A Longitudinal Study of the Visual Studio Code Community

Abstract

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.

Explore related subjects

Keep this discovery

BibTeXRIS

Panida Rumriankit, Akito Monden, Hiroki Inayoshi, Pattara Leelaprute, Bundit Manaskasemsak, Kundjanasith Thonglek, Arnon Rungsawang. 2026-09-04. How Developers Discuss Generative AI: A Longitudinal Study of the Visual Studio Code Community. https://arxiv.org/abs/2609.04680

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

The Impact of GenAI on the Future of Requirements Engineering

Recent advances in artificial intelligence (AI), particularly large language models (LLMs), are transforming how we design and build systems by increasing access to domain knowledge and by providing automation support to software engineering (SE). As implementation becomes less expensive through generalist SE agents, engineering effort shifts away from writing correct code and toward expressing, curating, verifying, and evaluating requirements. In this paper, we survey the state of the art in AI for requirements engineering (RE) research leading up to the transformation, before reviewing advances in LLMs. We survey two subsequent research areas: prompt programming, which treats LLM instructions as a program in SE vernacular, and generalist SE agents, which combine multiple LLM advances to yield semi-autonomous processes that complete SE tasks. Finally, we explore the future of requirements engineering along two axes: matters changing how we interact with requirements through the SE process, and matters changing how requirements are experienced by software developers and stakeholders more broadly, including end-users. This article aims to inform how RE researchers can navigate this transformation in the selection of future research priorities.

cs.SE

From Prompting to Engineering: A Research Agenda for Prompt Engineering in Software Engineering

Prompt engineering is increasingly used across Software Engineering (SE) activities, including requirements analysis, coding, testing, documentation, repository analysis, and planning. Yet prompts and related instruction artifacts are often created and evolved through task-specific and informal practices, with limited support for their systematic evaluation, management, traceability, and governance. To examine how SE can contribute to the maturation of these practices, we organized a structured community discussion at the First International Workshop on Empirical Prompt Engineering for Software Engineering (PROMPT-SE), co-located with EASE 2026. Participants discussed current prompting practices, challenges to their adoption and evaluation, and future directions for integrating prompt engineering into software development. We synthesized these discussions into five areas: prompt artifacts and standardization; evaluation and benchmarking; lifecycle integration; human-AI collaboration and skills; and governance, privacy, and technical debt. Based on these areas, we outline a research agenda to move prompt engineering from predominantly ad hoc interactions toward more systematic, maintainable, evaluable, traceable, and governable SE practices.

cs.SE

Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers

AI-assisted qualitative data analysis (QDA) offers unprecedented opportunities to streamline software engineering (SE) research, yet uncritical use risks compromising analytical rigor and flooding the field with accelerated production of low-quality research. While tactical best practices will naturally evolve over time, SE researchers currently lack strategic guidance to identify and mitigate methodological risks when attempting AI-assisted QDA. Based on our decades of qualitative SE research expertise and experience combined with an understanding of the emerging landscape of AI-assisted QDA, this paper presents a catalog of antipatterns in AI-assisted QDA - a set of assumptions and practices that initially appear advantageous but ultimately undermine analytical rigor and validity. The antipatterns are grouped into three categories reflecting escalating impact: Dangerous Drivers, Operational Missteps, and Analytical Failures. As more SE researchers attempt AI-assisted QDA, these antipatterns will help them identify and avoid common temptations and pitfalls, while reviewers can be equipped with the vocabulary and criteria to call out problematic and failed practice. Ultimately, this catalog of antipatterns can serve as a stepping stone in our responsible methodological evolution toward principled and meaningful human-AI collaboration in qualitative research.

cs.SE