arXiv subjects

Tien Rahayu Tulili

Publications and source records attributed to Tien Rahayu Tulili.

3 recordsLinked to original sources

arXiv2026

Machine Learning Models for the Early Detection of Burnout in Software Engineering: a Systematic Literature Review

Burnout is an occupational syndrome that, like many other professions, affects the majority of software engineers. Past research studies showed important trends, including an increasing use of machine learning techniques to allow for an early detection of burnout. This paper is a systematic literature review (SLR) of the research papers that proposed machine learning (ML) approaches, and focused on detecting burnout in software developers and IT professionals. Our objective is to review the accuracy and precision of the proposed ML techniques, and to formulate recommendations for future researchers interested to replicate or extend those studies. From our SLR we observed that a majority of primary studies focuses on detecting emotions or utilise emotional dimensions to detect or predict the presence of burnout. We also performed a cross-sectional study to detect which ML approach shows a better performance at detecting emotions; and which dataset has more potential and expressivity to capture emotions. We believe that, by identifying which ML tools and datasets show a better performance at detecting emotions, and indirectly at identifying burnout, our paper can be a valuable asset to progress in this important research direction.

cs.SE↗

arXiv2025

Exploring turnover, retention and growth in an OSS Ecosystem

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. This study investigates the interplay between developer sentiment and workforce dynamics in Gentoo. The primary objectives are to (1) compare workforce metrics (turnover, retention, and growth rates) between sentiment-positive (SP) and sentiment-negative (SN) components, (2) examine temporal trends across three time phases, and (3) analyze the impact of these dynamics on software stability. A mixed-method approach was employed, integrating sentiment analysis of mailing lists and commit histories using the SentiStrength-SE tool. Workforce metrics were statistically analyzed using Pearson Correlation Matrix and Mann-Whitney U tests. The analysis focused on the most SP and SN components in the ecosystem. SN components exhibited higher retention rates but slower growth and turnover compared to SP components, which showed dynamic contributor behavior but reduced long-term stability. Temporal analysis revealed significant variations in workforce dynamics over three phases, with developer retention correlating positively with modifications in both sentiment groups. Tailored strategies are necessary for managing sentiment-driven dynamics in OSS projects. Improving \textit{adaptability} in SN components, and \textit{continuity} in SP components, could improve project sustainability and innovation. This study contributes to a nuanced understanding of sentiment's role in workforce behavior and software stability within OSS ecosystems.

cs.SE↗

arXiv2024

Characterising Developer Sentiment in Software Components: An Exploratory Study of Gentoo

Collaborative software development happens in teams, that cooperate on shared artefacts, and discuss development on online platforms. Due to the complexity of development and the variety of teams, software components often act as effective containers for parallel work and teams. Past research has shown how communication between team members, especially in an open-source environment, can become extremely toxic, and lead to members leaving the development team. This has a direct effect on the evolution and maintenance of the project in which the former members were active in. The purpose of our study is two-fold: first, we propose an approach to evaluate, at a finer granularity, the positive and negative emotions in the communication between developers; and second, we aim to characterise a project's development paths, or components, as more or less impacted by the emotions. Our analysis evaluates single sentences rather than whole messages as the finest granularity of communication. The previous study found that the high positivity or negativity at the sentence level may indirectly impact the writer him/herself, or the reader. In this way, we could highlight specific paths of Gentoo as the most affected by negative emotions, and show how negative emotions have evolved and changed along the same paths. By joining the analysis of the mailing lists, from which we derive the sentiment of the developers, with the information derived from the development logs, we obtained a longitudinal picture of how development paths have been historically affected by positive or negative emotions. Our study shows that, in recent years, negative emotions have generally decreased in the communication between Gentoo developers. We also show how file paths, as collaborative software development artefacts, were more or less impacted by the emotions of the developers.

cs.SE↗