Emotions are known to impact cognitive skills, thus influencing job performance. This is also true for software development, which requires creativity and problem-solving abilities. In this paper, we report the results of a field study involving professional developers from five different companies. We provide empirical evidence that a link exists between emotions and perceived productivity at the workplace. Furthermore, we present a taxonomy of triggers for developers' positive and negative emotions, based on the qualitative analysis of participants' self-reported answers collected through daily experience sampling. Finally, we experiment with a minimal set of non-invasive biometric sensors that we use as input for emotion detection. We found that positive emotional valence, neutral arousal, and high dominance are prevalent. We also found a positive correlation between emotional valence and perceived productivity, with a stronger correlation in the afternoon. Both social and individual breaks emerge as useful for restoring a positive mood. Furthermore, we found that a minimum set of non-invasive biometric sensors can be used as a predictor for emotions, provided that training is performed on an individual basis. While promising, our classifier performance is not yet robust enough for practical usage. Further data collection is required to strengthen the classifier, by also implementing individual fine-tuning of emotion models.

Emotions and Perceived Productivity of Software Developers at the Workplace

Daniela Girardi;Filippo Lanubile;Nicole Novielli
;
2021-01-01

Abstract

Emotions are known to impact cognitive skills, thus influencing job performance. This is also true for software development, which requires creativity and problem-solving abilities. In this paper, we report the results of a field study involving professional developers from five different companies. We provide empirical evidence that a link exists between emotions and perceived productivity at the workplace. Furthermore, we present a taxonomy of triggers for developers' positive and negative emotions, based on the qualitative analysis of participants' self-reported answers collected through daily experience sampling. Finally, we experiment with a minimal set of non-invasive biometric sensors that we use as input for emotion detection. We found that positive emotional valence, neutral arousal, and high dominance are prevalent. We also found a positive correlation between emotional valence and perceived productivity, with a stronger correlation in the afternoon. Both social and individual breaks emerge as useful for restoring a positive mood. Furthermore, we found that a minimum set of non-invasive biometric sensors can be used as a predictor for emotions, provided that training is performed on an individual basis. While promising, our classifier performance is not yet robust enough for practical usage. Further data collection is required to strengthen the classifier, by also implementing individual fine-tuning of emotion models.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/369575
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