New Research Catalogs Pitfalls in AI-Assisted Qualitative Analysis for Software Studies

Software engineering researchers exploring AI-assisted qualitative data analysis (QDA) face a growing risk: the promise of speed and efficiency can mask serious analytical flaws that threaten research validity. A new paper from the arXiv preprint server lays out a structured guide to the most common pitfalls researchers should watch for.

The Problem with Uncritical Adoption#

While AI tools can accelerate the laborious work of coding interviews, survey responses, and documents, simply deploying them without careful thought risks a flood of low-quality research into the SE field. Researchers accustomed to manual, reflective analysis often struggle to identify where AI shortcuts might have silently eroded their methodology's soundness.

A Catalog of Antipatterns#

The paper presents what researchers call "antipatterns"—practices that initially look smart and efficient but ultimately weaken rigor and validity. These are organized into three tiers:

• Dangerous Drivers: foundational assumptions that misdirect the entire analysis
• Operational Missteps: execution errors that compound throughout the work
• Analytical Failures: breaches of logic or evidence interpretation that invalidate conclusions

Why This Matters Now#

As more SE researchers adopt AI assistance in qualitative work, having a shared vocabulary around methodological risks becomes critical. The framework equips both researchers and peer reviewers with concrete criteria to recognize when AI tools have been applied responsibly versus when they've compromised integrity.

The authors, drawing on decades of collective experience in qualitative SE research, frame this catalog as a waypoint—not a final word, but essential scaffolding for the field to develop principled practices around human-AI collaboration in evidence-based research.

Read the full paper on arXiv