The Gentoo ecosystem has evolved significantly over 23 years, highlighting the critical impact of developer sentiment on workforce dynamics such as turnover, retention, and growth. While prior research has explored sentiment at the project level, sentiment-driven dynamics at the component level remain underexplored, particularly in their implications for software stability.

ABSTRACT Background Developers, who are the main driving force behind software development, have central work in handling software components such as modules, libraries, and frameworks, which are the backbone of a project's architecture. Managing these components well ensures the smooth implementation of new features, maintains system integrity, and addresses dependencies efficiently. This clearly needs effective team collaboration as well as task management that, in the end, may significantly boost productivity. Additionally, as human beings, emotional involvement during development may naturally deeply influence developers' interaction and productivity. The emotions expressed in negative and positive sentiments may be triggered by different causes, including successfully overcoming obstacles, receiving positive feedback, facing challenges, or demanding tight deadlines. Objective In this study, we investigated the three aspects (e.g., developers, software components, and sentiments), specifically focusing on how developers' sentiments affect the components they work on or vice versa. Methods We conducted a structured analysis of Gentoo's means of communication, the mailing list of technical discussions, and the developers' activity (e.g., commits) during the software development over 23 years. Results We characterized the components that were affected by sentiments and found that there are differences in the developers' activity in the affected components. Conclusion This study offers implications mainly for investigating the sentiments expressed on the granular level of components and further scrutinizing the components and developers' activity. In addition, our study offers a structured approach that applies to any software project where written communication and development logs are available and several hypotheses that may direct to future works.

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. 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). 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. 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. 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.