Qualitative Data Analysis in Software Engineering: Techniques and Teaching Insights

TL;DR

This paper discusses qualitative data analysis techniques for software engineering artifacts, emphasizing coding methods, quality assurance principles, and best practices to improve research depth and credibility.

Contribution

It introduces qualitative coding processes and quality assurance principles tailored for software engineering research, enhancing interpretative rigor and educational value.

Findings

Qualitative coding enhances data interpretation accuracy.

Quality principles improve research credibility.

Best practices support rigorous qualitative analysis.

Abstract

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.