DeepSoft: A vision for a deep model of software

Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification problems, as opposed to predicting future events. We present a vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling software and its development process to predict future risks and recommend interventions. DeepSoft, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term temporal dependencies that occur in software evolution. Such deep learned patterns of software can be used to address a range of challenging problems such as code and task recommendation and prediction. DeepSoft provides a new approach for research into modeling of source code, risk prediction and mitigation, developer modeling, and automatically generating code patches from bug reports.Comment: FSE 201

Learning, Continuity and Change in Adult Life [Wider Benefits of Learning Research Report No. 3]

This report presents results from extensive fieldwork carried out by the Wider Benefits of Learning research team. It presents an original analytical framework developed specifically for this study, combined with empirical results from 140 in-depth biographical interviews in three different areas of England. The interviews explore the way learning affects people’s health and well-being; their family lives; and their engagement in civic activity. The report addresses these effects at both an individual and collective level. It concludes with a set of significant policy implications

A Generic Technique for Domain-Specific Visual Language Model Refactoring to Patterns

As the popularity of domain-specific visual languages (DSVLs) grows, concerns have arisen regarding quality assurance and evolvability of their meta-models and model instances. In this paper we address aspects of automated DSVL model instance modification for quality improvement based on refactoring specifications. We propose a graph transformation-based visual language approach for DSVL authors to specify the matching and discovery of DSVL “bad model smells” and the application of pattern-based solutions in a DSVL meta-tool. As an outcome, DSVL users are provided with pattern-based design evolution support as refactorings for their DSVL-based domain models

Investigating the effects of personality traits on pair programming in a higher education setting through a family of experiments

Evidence from our systematic literature review revealed numerous inconsistencies in findings from the Pair Programming (PP) literature regarding the effects of personality on PP’s effectiveness as a pedagogical tool. In particular: i) the effect of differing personality traits of pairs on the successful implementation of pair-programming (PP) within a higher education setting is still unclear, and ii) the personality instrument most often used had been Myers-Briggs Type Indicator (MBTI), despite being an indicator criticized by personality psychologists as unreliable in measuring an individual’s personality traits. These issues motivated the research described in this paper. We conducted a series of five formal experiments (one of which was a replicated experiment), between 2009 and 2010, at the University of Auckland, to investigate the effects of personality composition on PP’s effectiveness. Each experiment looked at a particular personality trait of the Five-Factor personality framework. This framework comprises five broad traits (Openness to experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism), and our experiments focused on three of these - Conscientiousness, Neuroticism, and Openness. A total of 594 undergraduate students participated as subjects. Overall, our findings for all five experiments, including the replication, showed that Conscientiousness and Neuroticism did not present a statistically significant effect upon paired students’ academic performance. However, Openness played a significant role in differentiating paired students’ academic performance. Participants’ survey results also indicated that PP not only caused an increase in satisfaction and confidence levels but also brought enjoyment to the tutorial classes and enhanced students’ motivation