Burnout in software engineering: A systematic mapping study

Context: Burnout is a work-related syndrome that, similar to many occupations, influences most software developers. For decades, studies in software engineering(SE) have explored the causes of burnout and its consequences among IT professionals. Objective: This paper is a systematic mapping study (SMS) of the studies on burnout in SE, exploring its causes and consequences, and how it is studied (e.g., choice of data). Method: We conducted a systematic mapping study and identified 92 relevant research articles dating as early as the early 1990s, focusing on various aspects and approaches to detect burnout in software developers and IT professionals. Results: Our study shows that early research on burnout was primarily qualitative, which has steadily moved to more quantitative, data-driven in the last decade. The emergence of machine learning (ML) approaches to detect burnout in developers has become a de-facto standard. Conclusion: Our study summarises what we now know about burnout, how software artifacts indicate burnout, and how machine learning can help its early detection. As a comprehensive analysis of past and present research works in the field, we believe this paper can help future research and practice focus on the grand challenges ahead and offer necessary tools.

Automated summary

This paper is a systematic mapping study of burnout research in software engineering. The study shows that early research on burnout was qualitative, but has now moved towards quantitative and data-driven approaches. Machine learning methods have become a de-facto standard for detecting burnout in software developers. The study summarizes our understanding of burnout, how software artifacts indicate burnout, and how machine learning can aid in its early detection.