A Cluster-based Approach for Emotion Recognition in Software Development

Timely recognizing developers’ emotions is crucial to support their productivity and well-being. To this aim, recent research has proposed approaches for sensor-based emotion detection during programming tasks, with promising results. However, limitations exist due to individual physiological differences, which we aim to address in the current study. Specifically, we propose a novel cluster-based approach for emotion recognition in software development using non-invasive biometric sensors. Our methodology enables the training of emotion recognition models specific to groups of people with similar physiological profiles. We evaluate our approach using a dataset of self-reported emotions and biometrics of developers involved in a Java programming task. Results show that our cluster-based approach enhances the performance of emotion classification compared to baseline approaches that do not account for individual differences. For valence recognition, we achieve improvements in precision (+33%), recall (+12%), and F1-score (+18%). For arousal, the most notable improvement is observed in precision (+29%). The classifier demonstrates particular effectiveness in identifying negative emotions with high arousal, which is especially valuable for detecting emotional states potentially detrimental to developers’ well-being. While promising, our results suggest that further optimization through additional data collection and individual model training would be beneficial before practical deployment. The study includes a comprehensive evaluation of the proposed approach and provides a lab package for replication purposes.