  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.
