An Empirical Study of Generative AI Adoption in Software Engineering

Objective. This study aims to provide an overview of the status of GenAI adoption in SE. It investigates the (1) status of GenAI adoption, (2) associated benefits and challenges, (3) institutionalization of tools and techniques, and (4) anticipated long-term impacts on SE professionals and the community.Method. We conducted an internationally distributed questionnaire-based survey to collect insights from SE practitioners. The survey combined closed-ended and open-ended questions to capture both quantitative trends and qualitative insights. We received 204 responses from 37 countries. Qualitative responses were analyzed using systematic coding and categorization. To cope with the random sampling limitation, we used bootstrapping and conservatively reported confidence intervals.Results. The results indicate a wide adoption of GenAI tools and that they are deeply integrated into daily SE work, particularly for implementation, verification and validation, personal assistance, and maintenance-related tasks. Practitioners report substantial benefits, most notably reduction in cycle time, enhanced support in knowledge work, perceived quality improvements and productivity gains. However, objective measurement of productivity and quality remains limited in practice. Significant challenges persist, including incorrect or unreliable outputs, prompt engineering difficulties, validation overhead, security and privacy concerns, and risks of overreliance. Institutionalization of tools and techniques seems to be common, but it varies considerably, with a strong focus on tool access and less emphasis on training and governance. Practitioners expect GenAI to redefine rather than replace their roles, while expressing moderate concern about job market contraction and skill shifts.