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Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers

arXiv 2608.27927 ↗First posted 28 Aug 2026cs.SE cs.AIcited by 0

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Abstract (arXiv)

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.

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References · 16

  1. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  2. 2025Guidelines for Empirical Studies in Software Engineering involving Large Language Modelscited by 21
  3. 2024Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysiscited by 20
  4. 2024Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Aheadcited by 17
  5. 2024Towards Evaluation Guidelines for Empirical Studies involving LLMscited by 15
  6. 2025Generative AI and Empirical Software Engineering: A Paradigm Shiftcited by 9
  7. 2024Applications and Implications of Large Language Models in Qualitative Analysis: A New Frontier for Empirical Software Engineeringcited by 8
  8. 2021Socio-Technical Grounded Theory for Software Engineeringcited by 7
  9. 2025LLM-Assisted Thematic Analysis: Opportunities, Limitations, and Recommendationscited by 5
  10. 2024Guiding Principles for Using Mixed Methods Research in Software Engineeringcited by 4
  11. 2024Large Language Model for Qualitative Research -- A Systematic Mapping Studycited by 4
  12. 2025Get on the Train or be Left on the Station: Using LLMs for Software Engineering Researchcited by 3
4 more
  1. 2025Generative Artificial Intelligence in Qualitative Research Methods: Between Hype and Risks?cited by 2
  2. 2025On the Use of Large Language Models for Qualitative Synthesiscited by 1
  3. 2026Qualitative Coding Analysis through Open-Source Large Language Models: A User Study and Design Recommendationscited by 1
  4. 2026Prompt Engineering Strategies for LLM-based Qualitative Coding of Psychological Safety in Software Engineering Communities: A Controlled Empirical Studycited by 1

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