AI辅助定性数据分析中的反模式：面向软件工程研究者的诱惑与陷阱目录
Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers
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中文总结 AI 辅助
本文针对软件工程研究者，梳理AI辅助定性数据分析中的三类反模式，助力研究者规避相关陷阱，为该领域负责任的方法学演进提供支撑。
中文摘要 AI 辅助
AI辅助定性数据分析（QDA）为软件工程（SE）研究提供了前所未有的流程简化机遇，但不加审慎地使用可能损害分析严谨性，并导致该领域充斥着大量低质量的加速产出研究。尽管战术层面的最佳实践会随时间自然演变，但目前SE研究者缺乏识别和缓解AI辅助QDA方法学风险的战略指导。基于作者数十年的定性SE研究专业知识与经验，结合对新兴AI辅助QDA格局的理解，本文提出了AI辅助QDA中的反模式目录——一组看似有利但最终会破坏分析严谨性与有效性的假设和实践。这些反模式按影响升级分为三类：危险驱动因素、操作失误和分析失败。随着更多SE研究者尝试AI辅助QDA，这些反模式将帮助他们识别并规避常见的诱惑与陷阱，同时评审者可借助相关术语和标准指出有问题的实践与失败的实践。最终，该反模式目录可成为负责任的方法学演进的垫脚石，推动定性研究中原则性且有意义的人-AI协作。
英文摘要
AI-assisted qualitative data analysis (QDA) offers unprecedented opportunities to streamline software engineering (SE) research, yet uncritical use risks compromising analytical rigor and flooding the field with accelerated production of low-quality research. While tactical best practices will naturally evolve over time, SE researchers currently lack strategic guidance to identify and mitigate methodological risks when attempting AI-assisted QDA. Based on our decades of qualitative SE research expertise and experience combined with an understanding of the emerging landscape of AI-assisted QDA, this paper presents a catalog of antipatterns in AI-assisted QDA - a set of assumptions and practices that initially appear advantageous but ultimately undermine analytical rigor and validity. The antipatterns are grouped into three categories reflecting escalating impact: Dangerous Drivers, Operational Missteps, and Analytical Failures. As more SE researchers attempt AI-assisted QDA, these antipatterns will help them identify and avoid common temptations and pitfalls, while reviewers can be equipped with the vocabulary and criteria to call out problematic and failed practice. Ultimately, this catalog of antipatterns can serve as a stepping stone in our responsible methodological evolution toward principled and meaningful human-AI collaboration in qualitative research.
