Marko Elovainio

Miikka Kuutila, Mika Mäntylä, Maëlick et al.

Reports of poor work well-being and fluctuating productivity in software engineering have been reported in both academic and popular sources. Understanding and predicting these issues through repository analysis might help manage software developers' well-being. Our objective is to link data from software repositories, that is commit activity, communication, expressed sentiments, and job events, with measures of well-being obtained with a daily experience sampling questionnaire. To achieve our objective, we studied a single software project team for eight months in the software industry. Additionally, we performed semi-structured interviews to explain our results. The acquired quantitative data are analyzed with generalized linear mixed-effects models with autocorrelation structure. We find that individual variance accounts for most of the $R^2$ values in models predicting developers' experienced well-being and productivity. In other words, using software repository variables to predict developers' well-being or productivity is challenging due to individual differences. Prediction models developed for each developer individually work better, with fixed effects $R^2$ value of up to 0.24. The semi-structured interviews give insights into the well-being of software developers and the benefits of chat interaction. Our study suggests that individualized prediction models are needed for well-being and productivity prediction in software development.

Miikka Kuutila, Mika Mäntylä, Maëlick Claes et al.

Background: The experience sampling method studies everyday experiences of humans in natural environments. In psychology it has been used to study the relationships between work well-being and productivity. To our best knowledge, daily experience sampling has not been previously used in software engineering. Aims: Our aim is to identify links between software developers self-reported affective states and work well-being and measures obtained from software repositories. Method: We perform an experience sampling study in a software company for a period of eight months, we use logistic regression to link the well-being measures with development activities, i.e. number of commits and chat messages. Results: We find several significant relationships between questionnaire variables and software repository variables. To our surprise relationship between hurry and number of commits is negative, meaning more perceived hurry is linked with a smaller number of commits. We also find a negative relationship between social interaction and hindered work well-being. Conclusions: The negative link between commits and hurry is counter-intuitive and goes against previous lab-experiments in software engineering that show increased efficiency under time pressure. Overall, our work is an initial step in using experience sampling in software engineering and validating theories on work well-being from other fields in the domain of software engineering.

Miikka Kuutila, Mika V. Mäntylä, Maëlick Claes et al.

During the past years, psychological diseases related to unhealthy work environments, such as burnouts, have drawn more and more public attention. One of the known causes of these affective problems is time pressure. In order to form a theoretical background for time pressure detection in software repositories, this paper combines interdisciplinary knowledge by analyzing 1270 papers found on Scopus database and containing terms related to time pressure. By clustering those papers based on their abstract, we show that time pressure has been widely studied across different fields, but relatively little in software engineering. From a literature review of the most relevant papers, we infer a list of testable hypotheses that we want to verify in future studies in order to assess the impact of time pressures on software developers mental health.