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Reports of poor work well-being and fluctuating productivity in softwareengineering have been reported in both academic and popular sources.Understanding and predicting these issues through repository analysis mighthelp manage software developers' well-being. Our objective is to link data fromsoftware repositories, that is commit activity, communication, expressedsentiments, and job events, with measures of well-being obtained with a dailyexperience sampling questionnaire. To achieve our objective, we studied asingle 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 linearmixed-effects models with autocorrelation structure. We find that individualvariance accounts for most of the $R^2$ values in models predicting developers'experienced well-being and productivity. In other words, using softwarerepository variables to predict developers' well-being or productivity ischallenging due to individual differences. Prediction models developed for eachdeveloper individually work better, with fixed effects $R^2$ value of up to0.24. The semi-structured interviews give insights into the well-being ofsoftware developers and the benefits of chat interaction. Our study suggeststhat individualized prediction models are needed for well-being andproductivity prediction in software development.

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