Do feelings matter? On the correlation of affects and the self-assessed productivity in software engineering

TL;DR

This study explores how developers' emotions, specifically valence, arousal, and dominance, correlate with their self-assessed productivity during programming tasks, highlighting the importance of psychological factors in software engineering.

Contribution

It introduces a methodology for measuring affects and their real-time correlation with productivity using linear mixed-effects models in software engineering.

Findings

Valence and dominance positively correlate with self-assessed productivity.

The linear mixed-effects model explains about 38% of the variance in productivity.

Lessons learned on applying psychological measurements in software engineering.

Abstract

Background: software engineering research (SE) lacks theory and methodologies for addressing human aspects in software development. Development tasks are undertaken through cognitive processing activities. Affects (emotions, moods, feelings) have a linkage to cognitive processing activities and the productivity of individuals. SE research needs to incorporate affect measurements to valorize human factors and to enhance management styles. Objective: analyze the affects dimensions of valence, arousal, and dominance of software developers and their real-time correlation with their self-assessed productivity (sPR). Method: repeated measurements design with 8 participants (4 students, 4 professionals), conveniently sampled and studied individually over 90 minutes of programming. The analysis was performed by fitting a linear mixed- effects (LME) model. Results: valence and dominance are positively correlated with the sPR. The model was able to express about 38% of deviance from the sPR. Many lessons were learned when employing psychological measurements in SE and for fitting LME. Conclusion: this article demonstrates the value of applying psychological tests in SE and echoes a call to valorize the human, individualized aspects of software developers. It reports a body of knowledge about affects, their classification, their measurement, and the best practices to perform psychological measurements in SE with LME models.