  Recent advancements in Large Language Models (LLMs) have enabled
collaborative human-bot interactions in Software Engineering (SE), similar to
many other professions. However, the potential benefits and implications of
incorporating LLMs into qualitative data analysis in SE have not been
completely explored. For instance, conducting qualitative data analysis
manually can be a time-consuming, effort-intensive, and error-prone task for
researchers. LLM-based solutions, such as generative AI models trained on
massive datasets, can be utilized to automate tasks in software development as
well as in qualitative data analysis. To this end, we utilized LLMs to automate
and expedite the qualitative data analysis processes. We employed a multi-agent
model, where each agent was tasked with executing distinct, individual research
related activities. Our proposed model interpreted large quantities of textual
documents and interview transcripts to perform several common tasks used in
qualitative analysis. The results show that this technical assistant speeds up
significantly the data analysis process, enabling researchers to manage larger
datasets much more effectively. Furthermore, this approach introduces a new
dimension of scalability and accuracy in qualitative research, potentially
transforming data interpretation methodologies in SE.
