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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 31·5 min read
Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers
TL;DR
A catalog of antipatterns in AI-assisted qualitative data analysis (QDA) highlights common pitfalls that can compromise research quality in software engineering. By identifying these issues, researchers can enhance their analytical rigor and avoid low-quality outcomes.
✦ Why It Matters
Review your AI-assisted QDA practices against the identified antipatterns to ensure research quality.
Key Takeaways
- Identify Dangerous Drivers that may mislead your analysis.
- Avoid Operational Missteps that can compromise data integrity.
- Recognize Analytical Failures that undermine research validity.
- Use the catalog as a checklist for your QDA practices.
Full Summary
AI-assisted qualitative data analysis (QDA) presents significant opportunities for software engineering (SE) research but also introduces risks that can undermine research quality. This paper catalogs antipatterns—misleading assumptions and practices—that SE researchers may encounter when using AI in QDA.
These antipatterns are categorized into three levels of impact: Dangerous Drivers, Operational Missteps, and Analytical Failures. By leveraging decades of qualitative SE research experience, the authors provide strategic guidance to help researchers recognize and avoid these pitfalls.
The catalog serves as a resource for both researchers and reviewers, promoting a more principled approach to human-AI collaboration in qualitative research. Ultimately, it aims to foster a responsible evolution in methodologies that enhance the validity of research findings.