Developers experience a wide range of emotions while creating software. Being able to identify the causes of one’s own and peers’ emotions can equip developers with the ability to regulate their behavior to restore positive moods and productivity. In this article, we investigate to what extent self-monitoring of emotions can enhance agile retrospective meetings by improving the emotion awareness of participants. To this aim, we conducted a controlled experiment involving three software development teams involving two student teams and one professional developers team. The experimental design involves the collection of biometrics and self-reported information about emotions, which are then visualized before the retrospective meetings to inform discussion using EmoVizPhy, a tool that we designed and implemented for this aim. While students found that self-monitoring helped them recall significant emotional episodes, leading to more meaningful contributions during retrospectives, professional developers perceived limited benefits from this practice. Furthermore, based on the analysis of corrective actions identified by the participants during the study, we hypothesize that self-monitoring of emotions through EmoVizPhy may play a valuable role in facilitating the consolidation of new agile teams for which roles and collaboration dynamics are still being defined.

Background. The widespread adoption of hybrid work following the COVID-19 pandemic has fundamentally transformed software development practices, introducing new challenges in communication and collaboration as organizations transition from traditional office-based structures to flexible working arrangements. This shift has established a new organizational norm where even traditionally office-first companies now embrace hybrid team structures. While remote participation in meetings has become commonplace in this new environment, it may lead to isolation, alienation, and decreased engagement among remote team members. Aims. This study aims to identify and characterize engagement patterns in hybrid meetings through objective measurements, focusing on the differences between co-located and remote participants. Method. We studied professionals from three software companies over several weeks, employing a multimodal approach to measure engagement. Data were collected through self-reported questionnaires and physiological measurements using biometric devices during hybrid meetings to understand engagement dynamics. Results. The regression analyses revealed comparable engagement levels between onsite and remote participants, though remote participants show lower engagement in long meetings regardless of participation mode. Active roles positively correlate with higher engagement, while larger meetings and afternoon sessions are associated with lower engagement. Conclusions. Our results offer insights into factors associated with engagement and disengagement in hybrid meetings, as well as potential meeting improvement recommendations. These insights are potentially relevant not only for software teams but also for knowledge-intensive organizations across various sectors facing similar hybrid collaboration challenges.

This PhD research project investigates whether emotional awareness can be helpful to improve developers’ productivity and well-being. The aim is to provide insights, based on self-monitoring, which can help developers to prevent or reduce stress. To support the research, a visualization tool has been built that shows biometrics and self-reported emotions. A series of empirical studies are conducted in the context of agile retrospective meetings, involving both students in a lab setting and professional developers in the workplace. Research will also address the use of non-invasive biometric sensors as an alternative rather than a complement to self-reporting of emotions. The aim is to enable emotional awareness without the need for intrusive self-monitoring mechanisms.

Early identification of emotions of software developers can enable timely intervention in order to support developers' well-being and prevent burnout. We present a machine learning experiment aimed at recognizing emotions during programming tasks using wearable biometric sensors, tracking electrodermal activity and heart-related metrics. As a gold standard for supervised learning, we rely on a state-of-the-art tool for emotion recognition based on facial expression analysis. We design, implement and evaluate an approach that combines the output of two classifiers for neutral valence recognition and positive/negative polarity classification. Our findings suggest that biometric sensors in a wristband can be used to identify emotions whose recognition would otherwise need an intrusive webcam.