Metraipolis › archive › dc_2e6aaa586ea0

Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers — arXiv:2608.27927

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.27927
License (as archived): CC0-1.0
Archived: 2026-10-17T10:48:01.214Z
SHA-256 of the archived text: 117129b5031675f84caf236c62ce3c120a46ba9c94c870ec30e91eb7268b8e48
Stable URL of this page: https://metraipolis.com/documento/dc_2e6aaa586ea0

How to cite

Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers — arXiv:2608.27927 — arXiv (en), https://arxiv.org/abs/2608.27927 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2e6aaa586ea0 (Metraipolis archive dc_2e6aaa586ea0, sha256 117129b5031675f84caf236c62ce3c120a46ba9c94c870ec30e91eb7268b8e48)

BibTeX

@misc{dc2e6aaa586ea0,
  title = {Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers — arXiv:2608.27927},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.27927},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2e6aaa586ea0; sha256 117129b5031675f84caf236c62ce3c120a46ba9c94c870ec30e91eb7268b8e48},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.27927. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-31 in cs.AI).
Categorie arXiv: Software Engineering (cs.SE) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.27927 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T10:33:42.483Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-31?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).