Taking a Pulse on How Generative AI is Reshaping the Software Engineering Research Landscape

TL;DR

This study surveys 457 software engineering researchers to understand how they use Generative AI, revealing widespread adoption, specific use cases, perceived benefits, risks, and governance needs in research practices.

Contribution

It provides the first large-scale empirical analysis of GenAI adoption in SE research, detailing use cases, perceptions, risks, and governance considerations.

Findings

GenAI use is widespread among SE researchers.

Usage is concentrated in writing and early research activities.

Researchers emphasize the need for clearer governance and responsible use guidelines.

Abstract

Context: Software engineering (SE) researchers increasingly study Generative AI (GenAI) while also incorporating it into their own research practices. Despite rapid adoption, there is limited empirical evidence on how GenAI is used in SE research and its implications for research practices and governance. Aims: We conduct a large-scale survey of 457 SE researchers publishing in top venues between 2023 and 2025. Method: Using quantitative and qualitative analyses, we examine who uses GenAI and why, where it is used across research activities, and how researchers perceive its benefits, opportunities, challenges, risks, and governance. Results: GenAI use is widespread, with many researchers reporting pressure to adopt and align their work with it. Usage is concentrated in writing and early-stage activities, while methodological and analytical tasks remain largely human-driven. Although productivity gains are widely perceived, concerns about trust, correctness, and regulatory uncertainty persist. Researchers highlight risks such as inaccuracies and bias, emphasize mitigation through human oversight and verification, and call for clearer governance, including guidance on responsible use and peer review. Conclusion: We provide a fine-grained, SE-specific characterization of GenAI use across research activities, along with taxonomies of GenAI use cases for research and peer review, opportunities, risks, mitigation strategies, and governance needs. These findings establish an empirical baseline for the responsible integration of GenAI into academic practice.