From Bugs to Decision Support – Leveraging Historical Issue Reports in Software Evolution

Software developers in large projects work in complex information landscapes and staying on top of all relevant software artifacts is an acknowledged challenge. As software systems often evolve over many years, a large number of issue reports is typically managed during the lifetime of a system, representing the units of work needed for its improvement, e.g., defects to fix, requested features, or missing documentation. Efficient management of incoming issue reports requires the successful navigation of the information landscape of a project. In this thesis, we address two tasks involved in issue management: Issue Assignment (IA) and Change Impact Analysis (CIA). IA is the early task of allocating an issue report to a development team, and CIA is the subsequent activity of identifying how source code changes affect the existing software artifacts. While IA is fundamental in all large software projects, CIA is particularly important to safety-critical development. Our solution approach, grounded on surveys of industry practice as well as scientific literature, is to support navigation by combining information retrieval and machine learning into Recommendation Systems for Software Engineering (RSSE). While the sheer number of incoming issue reports might challenge the overview of a human developer, our techniques instead benefit from the availability of ever-growing training data. We leverage the volume of issue reports to develop accurate decision support for software evolution. We evaluate our proposals both by deploying an RSSE in two development teams, and by simulation scenarios, i.e., we assess the correctness of the RSSEs' output when replaying the historical inflow of issue reports. In total, more than 60,000 historical issue reports are involved in our studies, originating from the evolution of five proprietary systems for two companies. Our results show that RSSEs for both IA and CIA can help developers navigate large software projects, in terms of locating development teams and software artifacts. Finally, we discuss how to support the transfer of our results to industry, focusing on addressing the context dependency of our tool support by systematically tuning parameters to a specific operational setting

What have we learnt from the challenges of (semi-) automated requirements traceability? A discussion on blockchain applicability.

Over the last 3 decades, researchers have attempted to shed light into the requirements traceability problem by introducing tracing tools, techniques, and methods with the vision of achieving ubiquitous traceability. Despite the technological advances, requirements traceability remains problematic for researchers and practitioners. This study aims to identify and investigate the main challenges in implementing (semi-)automated requirements traceability, as reported in the recent literature. A systematic literature review was carried out based on the guidelines for systematic literature reviews in software engineering, proposed by Kitchenham. We retrieved 4530 studies by searching five major bibliographic databases and selected 70 primary studies. These studies were analysed and classified according to the challenges they present and/or address. Twenty-one challenges were identified and were classified into five categories. Findings reveal that the most frequent challenges are technological challenges, in particular, low accuracy of traceability recovery methods. Findings also suggest that future research efforts should be devoted to the human facet of tracing, to explore traceability practices in organisational settings, and to develop traceability approaches that support agile and DevOps practices. Finally, it is recommended that researchers leverage blockchain technology as a suitable technical solution to ensure the trustworthiness of traceability information in interorganisational software projects.publishedVersio

CURRENT ISSUES AFFECTING TRADE AND TRADE POLICY: AN ANNOTATED LITERATURE REVIEW

This review provides a base of literature describing current issues and research on the impacts of lobalization and the industrialization of agriculture and recent approaches to analyze and model agricultural trade and trade policies. Three key factors of the survey are differentiated goods, global economic integration and international supply chain linkages. The review covers 182 publications, which are presented alphabetically by author with a brief annotation describing how it relates to the above criteria. The articles are also indexed by keyword. A brief summary highlights the documented literature and includes a series of issues for future discussion and research.International Relations/Trade,

Design science research with focus groups - a pragmatic meta-model

Purpose – The purpose of this research paper is to present a pragmatic and systematic approach to conduct and document Design Science Research (DSR) activities with Focus Groups (FG), exploring its continuous usage and providing traceability between problem, requirements, solutions, and artifacts. Design/methodology/approach – To conduct the research and produce the meta-model for DSR with FG, a DSR approach was adopted using a conceptual model for Action Design Research already available. The artifact is the result from a specific literature review to define requirements, a careful design, and a refinement stage where it was widely used and tested in real IS implementation projects. Findings – The main outcome of this research is a specific meta-model for DSR with FG, that delivers new insights and practical guidelines for academics and professionals conducting and documenting real-world research and development initiatives deep-rooted in stakeholders’ participation. Rigorous and committed stakeholder engagement is a critical success factor in complex projects. The use of a meta-model enables to increase the process effectiveness, by framing debate and stimulating stakeholders’ participation. Research limitations/implications – The meta-model has been endorsed as a practical and useful artifact by the stakeholders participating in the IS projects where it was adopted. However, to fully demonstrate its capabilities and to become more robust, the model must be further used and tested in other application situations and environments. Originality/value – The usage of Focus Groups (FG) in Design Science Research (DSR) has already been proposed as an effective way, either to study artifacts, to propose improvements in its design, or to acknowledge the utility of those artifacts in field use. The paper provides a sound contribution to this line of research by presenting a meta-model that integrates process and data that may be used by researchers and practitioners to conduct their projects.info:eu-repo/semantics/acceptedVersio

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India