Stuck and Frustrated or in Flow and Happy: Sensing Developers' Emotions and Progress

Abstract

Software developers working on change tasks commonly experience a broad range of emotions, ranging from happiness all the way to frustration and anger. Research, primarily in psychology, has shown that for certain kinds of tasks, emotions correlate with progress and that biometric measures, such as electro-dermal activity and electroencephalography data, might be used to distinguish between emotions. In our research, we are building on this work and investigate developers' emotions, progress and the use of biometric measures to classify them in the context of software change tasks. We conducted a lab study with 17 participants working on two change tasks each. Participants were wearing three biometric sensors and had to periodically assess their emotions and progress. The results show that the wide range of emotions experienced by developers is correlated with their progress on the change tasks. Our analysis also shows that we can build a classifier to distinguish between positive and negative emotions in 71.36% and between low and high progress in 67.70% of all cases. These results open up opportunities for improving a developer's productivity. For instance, one could use such a classifier for providing recommendations at opportune moments when a developer is stuck and making no progress. Abstract-Software developers working on change tasks commonly experience a broad range of emotions, ranging from happiness all the way to frustration and anger. Research, primarily in psychology, has shown that for certain kinds of tasks, emotions correlate with progress and that biometric measures, such as electro-dermal activity and electroencephalography data, might be used to distinguish between emotions. In our research, we are building on this work and investigate developers' emotions, progress and the use of biometric measures to classify them in the context of software change tasks. We conducted a lab study with 17 participants working on two change tasks each. Participants were wearing three biometric sensors and had to periodically assess their emotions and progress. The results show that the wide range of emotions experienced by developers is correlated with their perceived progress on the change tasks. Our analysis also shows that we can build a classifier to distinguish between positive and negative emotions in 71.36% and between low and high progress in 67.70% of all cases. These results open up opportunities for improving a developer's productivity. For instance, one could use such a classifier for providing recommendations at opportune moments when a developer is stuck and making no progress.

Leveraging Biometric Data to Boost Software Developer Productivity

“…The biometric measures with the most predictive power for progress was thereby again a combination of all three biometric sensors: EDA tonic signal, skin temperature, brainwave frequency bands and pupil size. Overall, the results of this study provide further evidence on the value that biometric data can have to measure aspects of a developer during work and that it also works for longer and more realistic change tasks [70].…”

“…For the data labeling, the outcome measure under analysis has to be assigned to the biometric data segments used for predicting the outcome measure. As an example from our research, Figure 4 presents biometric data-EDA and HR-that we collected during one of our studies in combination with the participant's valence and progress ratings that it was then labeled with [70]. Valence refers to the positive or negative character of an experienced emotion, the higher the rating the more positive the emotion.…”

“…Recently, especially with the advances in sensor technology and the availability of more affordable biometric sensors, a few software engineering researchers have also looked at other aspects, for instance, using measures of cerebral blood flow, measures of sub-vocal utterances captured with electromyography (EMG), or EDA, eye-tracking and brain activity measures to asses task difficulty of small code snippets or programming tasks [67]- [69]. In a few cases, biometric measures have also been used for studies with software developers on longer and more realistic development tasks or even in the field [70]- [72].…”

“…In one of our studies, we examined the use of biometric data to assess a developer's progress and emotions, especially since research in psychology has already linked progress and emotions [70]. For this study, we had 17 participants (6 professional software developers, 11 Computer Science PhD students) work on two change tasks on open source systems for 30 minutes each in a quiet lab while again sitting in front of an eye-tracker and wearing a wristband to track EDA and heart-related measures as well as an EEG sensor.…”

Towards the participant observation of emotions in software developers teams

“…A novel approach to recognizing the emotions of software developers was proposed by Muller et al In two experiments, they used biofeedback devices to gather EEG, EDA, fMRI and eye-tracker signals of developers during their work [8]. Biometric signals provide objective information about reactions of the human body to emotional stimuli.…”