Human-AI Collaboration in Software Development: A Mixed-Methods Study of Developers’ Use of GitHub Copilot and ChatGPT

Abstract

The integration of AI-powered coding assistants into software development presents both opportunities and challenges. These tools promise enhanced productivity and code quality while introducing significant concerns related to workflow integration, accuracy, reliability, and ethical considerations. This study investigates how software developers interact with Generative AI (GenAI) tools in a large-scale public sector organization. Using a mixed-methods approach, we analyze 13 interviews and a survey with 114 respondents to explore the impact of GitHub Copilot (IDE-integrated AI) and ChatGPT (conversational AI assistant) on software engineering workflows. The Human-AI Collaboration and Adaptation Framework (HACAF) is central to our analysis, which integrates insights from several technology acceptance models and theories. Our findings reveal that compatibility within existing workflows plays a crucial role in effectively utilizing GenAI tools. We explore individual, technological, and social factors that influence the adoption of these tools, highlighting the importance of organizational support and collective user patterns within teams. By studying both users and non-users, we provide insights into the factors driving GenAI adoption and the barriers preventing full integration. Our findings contribute to discussions on AI-enhanced IDEs, human-AI collaboration in coding, and the evolving role of LLM-based assistants in modern software development.

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