457位顶会作者调研揭示生成式AI在软件工程研究中的真实使用图景。
Taking a Pulse on How Generative AI is Reshaping the Software Engineering Research Landscape
- 通过大规模问卷分析研究人员使用生成式AI的场景与动因。
- 写作和早期研究阶段广泛使用，但方法与分析仍以人工为主。
- 关注误判、偏见风险，呼吁加强人类审核与学术治理规范。
背景：软件工程（SE）研究者日益将生成式AI（GenAI）纳入研究主题与实践流程。尽管其应用迅速扩展，但关于其在SE研究中实际使用情况及对研究范式与治理影响的实证证据仍有限。目标：对2023至2025年间发表于顶级会议的457位SE研究者开展大规模调查。方法：结合定量与定性分析，考察谁在用GenAI、为何使用、在哪些研究环节使用，以及对其效益、机遇、挑战、风险与治理的看法。结果：GenAI使用广泛，许多研究者感受到采用压力并需与其研究对齐；使用集中在写作与早期活动，方法与分析仍以人工主导；虽普遍感知到效率提升，但对可信度、正确性与监管不确定性仍存担忧；主要风险包括错误信息与偏见，强调通过人工监督与验证来缓解，并呼吁建立更清晰的治理框架，涵盖负责任使用指南与同行评审机制。结论：本文提供了一幅细致入微的、面向软件工程领域的生成式AI使用全景图，构建了研究与同行评审中使用案例、机遇、风险、缓解策略与治理需求的分类体系，为学术实践中负责任整合生成式AI奠定了实证基础。
原文摘要 · Abstract (English)
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.
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