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
Individual differences limit predicting well-being and productivity using software repositories : a longitudinal industrial study
|Author:||Kuutila, Miikka1; Mäntylä, Mika1; Claes, Mäelick1;|
1M3S, ITEE, University of Oulu, Oulu, Finland
2Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
3School of Computing, Queen’s University, Kingston, Ontario, Canada
|Online Access:||PDF Full Text (PDF, 0.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021090645184
|Publish Date:|| 2021-09-06
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.
Empirical software engineering
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
The first, second and third author have been supported by Academy of Finland grant 298020. The first author has been supported by Kaute-foundation.
|Academy of Finland Grant Number:||
298020 (Academy of Finland Funding decision)
© The Authors 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.