Individual differences limit predicting well-being and productivity using software repositories : a longitudinal industrial study
Kuutila, Miikka; Mäntylä, Mika; Claes, Mäelick; Elovainio, Marko; Adams, Bram (2021-06-26)
Kuutila, M., Mäntylä, M., Claes, M. et al. Individual differences limit predicting well-being and productivity using software repositories: a longitudinal industrial study. Empir Software Eng 26, 88 (2021). https://doi.org/10.1007/s10664-021-09977-1
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https://urn.fi/URN:NBN:fi-fe2021090645184
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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 R2 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 R2 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.
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