GenAI Is No Silver Bullet for Qualitative Research in Software Engineering

Qualitative research gives rich insights into the quintessentially human aspects of software engineering as a socio-technical system. Qualitative research spans diverse strategies and methods, from interpretivist, in situ observational field studies, to deductive coding of data from mining studies. Advances in large language models and generative AI (GenAI) have prompted claims that artificial intelligence could automate qualitative analysis. Such claims are overgeneralizing from narrow successes. GenAI support must be carefully adapted to the data of interest, but also to the characteristics of a particular research strategy. In this Frontiers of SE paper , we discuss the emerging use of GenAI in relation to the broad spectrum of qualitative research in software engineering. We outline the dimensions of qualitative work in software engineering, scan limited emerging empirical evidence for GenAI assistance, examine the promises and pitfalls of GenAI-assisted qualitative research, and revisit qualitative research quality factors, in light of GenAI. Our goal is to inform researchers about the promises and pitfalls of GenAI-assisted qualitative research. We conclude with a research agenda to advance understanding of its use in software engineering.