TY - JOUR
T1 - Burnout in software engineering
T2 - A systematic mapping study
AU - Tulili, Tien Rahayu
AU - Capiluppi, Andrea
AU - Rastogi, Ayushi
N1 - Funding Information:
Tien Rahayu Tulili gratefully acknowledges scholarship funding for this research from the Indonesia Endowment Fund for Education(LPDP), Ministry of Finance of Republic of Indonesia , Ref. Number S-2566/LPDP.4/2021 .
Publisher Copyright:
© 2022 The Author(s)
PY - 2023/3
Y1 - 2023/3
N2 - 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.
AB - 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.
KW - Burnout
KW - Software engineering
KW - Systematic mapping study
UR - http://www.scopus.com/inward/record.url?scp=85145552474&partnerID=8YFLogxK
U2 - 10.1016/j.infsof.2022.107116
DO - 10.1016/j.infsof.2022.107116
M3 - Review article
AN - SCOPUS:85145552474
SN - 0950-5849
VL - 155
JO - Information and Software Technology
JF - Information and Software Technology
M1 - 107116
ER -