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

An agent-oriented approach to change propagation in software evolution

Software maintenance and evolution are inevitable activities since almost all software that is useful and successful stimulates user-generated requests for change and improvements. One of the most critical problems in software maintenance and evolution is to maintain consistency between software artefacts by propagating changes correctly. Although many approaches have been proposed, automated change propagation is still a significant technical challenge in software engineering. In this paper we present a novel, agent-oriented approach to deal with change propagation in evolving software systems that are developed using the Prometheus methodology. A metamodel with a set of the Object Constraint Language (OCL) rules forms the basis of the proposed framework. The underlying change propagation mechanism of our framework is based on the well-known Belief-Desire-Intention (BDI) agent architecture. Traceability information and design heuristics are also incorporated into the framework to facilitate the change propagation process

On Business Services Representation – The 3 x 3 x 3 Approach

The increasing popularity and influence of service-oriented computing give rise to the need of representational and methodological supports for the development and management of business services. From an IT perspective, there is a proliferation of methods and languages for representing Web services. Unfortunately, there has not been much work in modeling high-level services from a business perspective. Modeling business services should arguably capture their inherent features, along with many other representational artifacts. We propose a novel approach for business services representation featuring a three-dimensional representational space of which dimensions stand for the service consumer, service provider and service context. We also discuss how the proposed representation approach provides methodological supports to the area of service orientation. Finally, we present in-progress work on the application of our approach

Adversarial Patch Generation for Automatic Program Repair

Automatic program repair (APR) has seen a growing interest in recent years with numerous techniques proposed. One notable line of research work in APR is search-based techniques which generate repair candidates via syntactic analyses and search for valid repairs in the generated search space. In this work, we explore an alternative approach which is inspired by the adversarial notion of bugs and repairs. Our approach leverages the deep learning Generative Adversarial Networks (GANs) architecture to suggest repairs that are as close as possible to human generated repairs. Preliminary evaluations demonstrate promising results of our approach (generating repairs exactly the same as human fixes for 21.2% of 500 bugs).Comment: Submitted to IEEE Software's special issue on Automatic Program Repair. Added reference

A Taxonomy for Mining and Classifying Privacy Requirements in Issue Reports

Digital and physical footprints are a trail of user activities collected over the use of software applications and systems. As software becomes ubiquitous, protecting user privacy has become challenging. With the increasing of user privacy awareness and advent of privacy regulations and policies, there is an emerging need to implement software systems that enhance the protection of personal data processing. However, existing privacy regulations and policies only provide high-level principles which are difficult for software engineers to design and implement privacy-aware systems. In this paper, we develop a taxonomy that provides a comprehensive set of privacy requirements based on two well-established and widely-adopted privacy regulations and frameworks, the General Data Protection Regulation (GDPR) and the ISO/IEC 29100. These requirements are refined into a level that is implementable and easy to understand by software engineers, thus supporting them to attend to existing regulations and standards. We have also performed a study on how two large open-source software projects (Google Chrome and Moodle) address the privacy requirements in our taxonomy through mining their issue reports. The paper discusses how the collected issues were classified, and presents the findings and insights generated from our study.Comment: Submitted to IEEE Transactions on Software Engineering on 23 December 202

Simultaneous nitrification/denitrification and trace organic contaminant (TrOC) removal by an anoxic-aerobic membrane bioreactor (MBR)

Simultaneous nitrification/denitrification and trace organic contaminant (TrOC) removal during wastewater treatment by an integrated anoxic-aerobic MBR was examined. A set of 30 compounds was selected to represent TrOCs that occur ubiquitously in domestic wastewater. The system achieved over 95% total organic carbon (TOC) and over 80% total nitrogen (TN) removal. In addition, 21 of the 30 TrOCs investigated here were removed by over 90%. Low oxidation reduction potential (i.e., anoxic/anaerobic) regimes were conducive to moderate to high (50% to 90%) removal of nine TrOCs. These included four pharmaceuticals and personal care products (primidone, metronidazole, triclosan, and amitriptyline), one steroid hormone (17β-estradiol-17-acetate), one industrial chemical (4-tert-octylphenol) and all three selected UV filters (benzophenone, oxybenzone, and octocrylene). Internal recirculation between the anoxic and aerobic bioreactors was essential for anoxic removal of remaining TrOCs. A major role of the aerobic MBR for TOC, TN, and TrOC removal was observed