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Amidst the headlines about the attention economy and the possible impacts of screen time, research investigating the complex relationship between digital technology usage and wellbeing has gained urgency. Researchers generally use a combination of surveys and automatic tracking tools to gather time and frequency of technology use. However, the focus of data analysis has been on measuring duration and frequency of usage rather than exploring behavioral patterns, possibly better indicators of mood states or stress levels. We propose a methodology for detecting behavioural patterns from digital footprints using a sequence pattern mining algorithm, and using these as features for predicting mood. Results show that our method can be used to analyze the relationship between digital usage and mood, and predict the latter with an accuracy of 80%, significantly above the baseline (71.1%). This method provides another angle to investigate digital technology usage in wellbeing-related research.INDEX TERMS Screen time, app usage, digital footprints, affective computing, mood detection.

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Digital technology influences behaviours, moods and wellbeing. The relationships are complex, but users are increasingly interested in finding how to balance a digital life with psychological wellbeing. We describe MindGauge, a mobile app that helps users collect and analyse data about moods and behaviours. In this study, we collected computer and smartphone usage and self-reports from 72 participants by using MindGauge and RescueTime. We examined the relationship between productivity, task-switching, mood and lifestyle. The results show that a measure of productivity is positively correlated with task-switching occurrences. Also, that more task-switching is associated with negative moods. A few lifestyle aspects, such as sleep quality, have a significant relationship with task-switching and productivity. We also contribute a mood detection model that utilise both digital footprints and lifestyle contexts, yielding a 87% accuracy. The study provides new results, and tools, to understand the impact of technology on wellbeing.

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Unit testing is one of the important software development steps to ensure the software’s quality. Despite its importance, unit testing is often neglected since it requires a significant amount of time and effort from the software developers to write them. Existing automated testing generating systems from past research still have shortcomings due to the Genetic Algorithm (GA) limitations to generate the appropriate unit test codes. This study explores the feasibility of using Generative Adversarial Networks (GAN) models to generate unit test code with the ability of GAN to cover GA’s drawbacks. We perform experimentations using four state-of-the-art GAN models to generate basic unit test codes and compare the results by analyzing the generated output codes using novel metrics proposed from past studies as well as performing qualitative evaluation on the generated outputs. The results show that the generated codes have satisfactory quality scores (BLEU-2 of around 99%) from the models and adequate diversity score (NLL-Div and NLL-Gen) in most models. Our study shows positive indications and potential in the use of GAN for automatic unit test code generation and suggests recommendations for future studies in GAN-based unit test code generation systems

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