Individual Differences Limit Predicting Well-being and Productivity Using Software Repositories: A Longitudinal Industrial Study
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2021
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| _version_ | 1866909228306792448 |
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| author | Kuutila, Miikka Mäntylä, Mika Maëlick Claes Elovainio, Marko Adams, Bram |
| author_facet | Kuutila, Miikka Mäntylä, Mika Maëlick Claes Elovainio, Marko Adams, Bram |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2104_13713 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Individual Differences Limit Predicting Well-being and Productivity Using Software Repositories: A Longitudinal Industrial Study Kuutila, Miikka Mäntylä, Mika Maëlick Claes Elovainio, Marko Adams, Bram Software Engineering 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. |
| title | Individual Differences Limit Predicting Well-being and Productivity Using Software Repositories: A Longitudinal Industrial Study |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2104.13713 |