Individual Differences Limit Predicting Well-being and Productivity Using Software Repositories: A Longitudinal Industrial Study

TL;DR

This longitudinal study shows that predicting software developers' well-being and productivity from repository data is limited by individual differences, highlighting the need for personalized models.

Contribution

The paper demonstrates that individual differences significantly impact the predictability of well-being and productivity, advocating for personalized prediction models in software engineering.

Findings

Individual variance explains most of the variance in well-being and productivity models.

Personalized models outperform general models, with R^2 up to 0.24.

Semi-structured interviews provide insights into developer well-being and communication benefits.

Abstract

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.