9,990 research outputs found

    Uncovering sustainability concerns in software product lines

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    Sustainable living, i.e., living within the bounds of the available environmental, social, and economic resources, is the focus of many present-day social and scientific discussions. But what does sustainability mean within the context of Software Engineering? In this paper we undertake a comprehensive analysis of 8 case studies to address this question within the context of a specific SE approach, Software Product Line Engineering (SPLE). We identify the sustainability-related characteristics that arise in present-day studies that apply SPLE. We conclude that technical and economic sustainability are in prime focus on the present SPLE practice, with social sustainability issues, where they relate to organisations, also addressed to a good degree. On the other hand, the issues related to the personal sustainability are less prominent, and environmental considerations are nearly completely amiss. We present feature models and cross-relations that result from our analysis as a starting point for sustainability engineering through SPLE, suggesting that any new development should consider how these models would be instantiated and expanded for the intended socio-technical system. The good representation of sustainability features in these models is also validated with two additional case studies

    Grand Challenges of Traceability: The Next Ten Years

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    In 2007, the software and systems traceability community met at the first Natural Bridge symposium on the Grand Challenges of Traceability to establish and address research goals for achieving effective, trustworthy, and ubiquitous traceability. Ten years later, in 2017, the community came together to evaluate a decade of progress towards achieving these goals. These proceedings document some of that progress. They include a series of short position papers, representing current work in the community organized across four process axes of traceability practice. The sessions covered topics from Trace Strategizing, Trace Link Creation and Evolution, Trace Link Usage, real-world applications of Traceability, and Traceability Datasets and benchmarks. Two breakout groups focused on the importance of creating and sharing traceability datasets within the research community, and discussed challenges related to the adoption of tracing techniques in industrial practice. Members of the research community are engaged in many active, ongoing, and impactful research projects. Our hope is that ten years from now we will be able to look back at a productive decade of research and claim that we have achieved the overarching Grand Challenge of Traceability, which seeks for traceability to be always present, built into the engineering process, and for it to have "effectively disappeared without a trace". We hope that others will see the potential that traceability has for empowering software and systems engineers to develop higher-quality products at increasing levels of complexity and scale, and that they will join the active community of Software and Systems traceability researchers as we move forward into the next decade of research

    Grand Challenges of Traceability: The Next Ten Years

    Full text link
    In 2007, the software and systems traceability community met at the first Natural Bridge symposium on the Grand Challenges of Traceability to establish and address research goals for achieving effective, trustworthy, and ubiquitous traceability. Ten years later, in 2017, the community came together to evaluate a decade of progress towards achieving these goals. These proceedings document some of that progress. They include a series of short position papers, representing current work in the community organized across four process axes of traceability practice. The sessions covered topics from Trace Strategizing, Trace Link Creation and Evolution, Trace Link Usage, real-world applications of Traceability, and Traceability Datasets and benchmarks. Two breakout groups focused on the importance of creating and sharing traceability datasets within the research community, and discussed challenges related to the adoption of tracing techniques in industrial practice. Members of the research community are engaged in many active, ongoing, and impactful research projects. Our hope is that ten years from now we will be able to look back at a productive decade of research and claim that we have achieved the overarching Grand Challenge of Traceability, which seeks for traceability to be always present, built into the engineering process, and for it to have "effectively disappeared without a trace". We hope that others will see the potential that traceability has for empowering software and systems engineers to develop higher-quality products at increasing levels of complexity and scale, and that they will join the active community of Software and Systems traceability researchers as we move forward into the next decade of research

    Scenarios for the development of smart grids in the UK: literature review

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    Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.

    Technology Readiness Levels for Machine Learning Systems

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    The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and ML (from research through product across domain areas), we have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" (MLTRL) framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for ML workflows, including key distinctions from traditional software engineering. Even more, MLTRL defines a lingua franca for people across teams and organizations to work collaboratively on artificial intelligence and machine learning technologies. Here we describe the framework and elucidate it with several real world use-cases of developing ML methods from basic research through productization and deployment, in areas such as medical diagnostics, consumer computer vision, satellite imagery, and particle physics

    Negotiating disciplinary boundaries in engineering problem-solving practice

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    Includes bibliographical referencesThe impetus for this research is the well-documented current inability of Higher Education to facilitate the level of problem solving required in 21st century engineering practice. The research contends that there is insufficient understanding of the nature of and relationship between the significantly different forms of disciplinary knowledge underpinning engineering practice. Situated in the Sociology of Education, and drawing on the social realist concepts of knowledge structures (Bernstein, 2000) and epistemic relations (Maton, 2014), the research maps the topology of engineering problem-solving practice in order to illuminate how novice problem solvers engage in epistemic code shifting in different industrial contexts. The aim in mapping problem-solving practices from an epistemological perspective is to make an empirical contribution to rethinking the theory/practice relationship in multidisciplinary engineering curricula and pedagogy, particularly at the level of technician. A novel and pragmatic problem-solving model - integrated from a range of disciplines - forms the organising framework for a methodologically pluralist case-study approach. The research design draws on a metaphor from the empirical site (modular automation systems) and sees the analysis of twelve matched cases in three categories. Case-study data consist of questionnaire texts, re-enactment interviews, expert verification interviews, and industry literature. The problem-solving model components (problem solver, problem environment, problem structure and problem-solving process) were analysed using, primarily, the Legitimation Code Theory concept of epistemic relations. This is a Cartesian plane-based instrument describing the nature of and relations between a phenomenon (what) and ways of approaching the phenomenon (how). Data analyses are presented as graphical relational maps of different practitioner knowledge practices in different contexts across three problem solving stages: approach, analysis and synthesis. Key findings demonstrate a symbiotic, structuring relationship between the 'what' and the 'how' of the problem in relation to the problem-solving components. Successful problem solving relies on the recognition of these relationships and the realisation of appropriate practice code conventions, as held to be legitimate both epistemologically and contextually. Successful practitioners engage in explicit code-shifting, generally drawing on a priori physics and mathematics-based knowledge, while acquiring a posteriori context-specific logic-based knowledge. High-achieving practitioners across these disciplinary domains demonstrate iterative code-shifting practices and discursive sensitivity. Recommendations for engineering education include the valuing of disciplinary differences and the acknowledgement of contextual complexity. It is suggested that the nature of engineering mathematics as currently taught and the role of mathematical thinking in enabling successful engineering problem-solving practice be investigated

    Network Based Strategic Automation:A methodology to guide and facilitate collaboration

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