AI正从工具变为主动合作者，重塑软件工程研究范式。
Generative AI and Empirical Software Engineering: A Paradigm Shift
- 将大语言模型视为协作伙伴，改变开发者与代码的交互方式。
- 传统研究方法面临数据模态、因果假设和可复现性挑战。
- 适合关注AI时代软件工程研究范式的学者与实践者。
大型语言模型（LLMs）与自主代理在软件工程中的应用标志着持久的范式转变。这些系统为工具设计、工作流编排和实证观察创造了新机遇，同时从根本上重塑了开发者的角色及所产出的产物。尽管传统实证方法仍为核心，但人工智能的快速演进带来了新的数据模态，改变了因果假设，并挑战了“开发者”“产物”“交互”等基础概念。当人类与AI代理日益协同创作时，社会与技术主体的界限模糊，研究结果的可复现性取决于模型更新与提示上下文。本文探讨了LLM融入软件工程如何颠覆既有研究范式，分析其对研究对象、方法理论、数据类型及有效性威胁的影响。旨在帮助实证软件工程社区调整研究问题、研究工具与验证标准，以适应一个AI不仅是工具，更是主动合作者的时代。
原文摘要 · Abstract (English)
The adoption of large language models (LLMs) and autonomous agents in software engineering marks an enduring paradigm shift. These systems create new opportunities for tool design, workflow orchestration, and empirical observation, while fundamentally reshaping the roles of developers and the artifacts they produce. Although traditional empirical methods remain central to software engineering research, the rapid evolution of AI introduces new data modalities, alters causal assumptions, and challenges foundational constructs such as "developer", "artifact", and "interaction". As humans and AI agents increasingly co-create, the boundaries between social and technical actors blur, and the reproducibility of findings becomes contingent on model updates and prompt contexts. This vision paper examines how the integration of LLMs into software engineering disrupts established research paradigms. We discuss how it transforms the phenomena we study, the methods and theories we rely on, the data we analyze, and the threats to validity that arise in dynamic AI-mediated environments. Our aim is to help the empirical software engineering community adapt its questions, instruments, and validation standards to a future in which AI systems are not merely tools, but active collaborators shaping software engineering and its study.
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