Qualitative Data Analysis in Software Engineering: Techniques and Teaching Insights
<|reference_start|>Qualitative Data Analysis in Software Engineering: Techniques and Teaching Insights: Software repositories are rich sources of qualitative artifacts, including source code comments, commit messages, issue descriptions, and documentation. These artifacts offer many interesting insights when analyzed through quantitative methods, as outlined in the chapter on mining software repositories. This chapter shifts the focus towards interpreting these artifacts using various qualitative data analysis techniques. We introduce qualitative coding as an iterative process, which is crucial not only for educational purposes but also to enhance the credibility and depth of research findings. Various coding methods are discussed along with the strategic design of a coding guide to ensure consistency and accuracy in data interpretation. The chapter also discusses quality assurance in qualitative data analysis, emphasizing principles such as credibility, transferability, dependability, and confirmability. These principles are vital to ensure that the findings are robust and can be generalized in different contexts. By sharing best practices and lessons learned, we aim to equip all readers with the tools necessary to conduct rigorous qualitative research in the field of software engineering.<|reference_end|>
@article{treude2024qualitative,
    title={Qualitative Data Analysis in Software Engineering: Techniques and
  Teaching Insights},
    author={Christoph Treude},
    journal={arXiv preprint arXiv:2406.08228},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08228},
    primaryClass={cs.SE}
}