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Abstract: Safety-critical systems manufacturers have the duty of care, i.e., they should take correct steps while performing acts that could foreseeably harm others. Commonly, industry standards prescribe reasonable steps in their process requirements, which regulatory bodies trust. Manufacturers perform careful documentation of compliance with each requirement to show that they act under acceptable criteri...
Abstract: Software engineering is decision intensive. Evidence‐based software engineering is suggested for decision‐making concerning the use of methods and technologies when developing software. Software development often includes the reuse of software assets, for example, open‐source components. Which components to use have implications on the quality of the software (e.g., maintainability). Thus, researc...
Abstract: [Context and motivation:] For realistic self-adaptive systems, multiple quality attributes need to be considered and traded off against each other. These quality attributes are commonly encoded in a utility function, for instance, a weighted sum of relevant objectives. [Question/problem:] The research agenda for requirements engineering for self-adaptive systems has raised the need for decision-ma...
Abstract: Availability of powerful computation and communication technology as well as advances in artificial intelligence enable a new generation of complex, AI-intense systems and applications. Such systems and applications promise exciting improvements on a societal level, yet they also bring with them new challenges for their development. In this paper we argue that significant challenges relate to defi...
Abstract: Availability of powerful computation and communication technology as well as advances in artificial intelligence enable a new generation of complex, AI-intense systems and applications. Such systems and applications promise exciting improvements on a societal level, yet they also bring with them new challenges for their development. In this paper we argue that significant challenges relate to defi...
Abstract: Using machine learning in clinical practice poses hard requirements on explainability, reliability, replicability and robustness of these systems. Therefore, developing reliable software for monitoring critically ill patients requires close collaboration between physicians and software engineers. However, these two different disciplines need to find own research perspectives in order to contribute...
Abstract: There has been tremendous growth in the Internet of Things (IoT) technologies, and many new applications have emerged. However, cascading failure as one of the major issues in such constrained networks have been neglected. In this paper, we apply an effective clustering approach dubbed as REFIT to enhance network topology robustness via nodes’ residual energy. The REFIT protocol divides the networ...
Abstract: This report presents and analyses the use of a megagame. Games with the primary aim to educate or enhance dialogue between different actors can be valuable for the engagement with and pedagogy of places and thus an interesting method for outdoor education and learning. The game discussed in this report has the capacity to gather between 40 and 100 participants and is thus considered to be a Megaga...
Abstract: Abstract This paper proposes a systematic three-stage methodology for migrating complex real-time industrial software systems from single-core to multi-core computing platforms. Single-core platforms have limited computational capabilities that prevent integration of computationally demanding applications such as image processing within the existing system. Modern multi-core processors offer a pro...
Abstract: Automated testing tools typically create test cases that are different from what human testers create. This often makes the tools less effective, the created tests harder to understand, and thus results in tools providing less support to human testers. Here, we propose a framework based on cognitive science and, in particular, an analysis of approaches to problem solving, for identifying cognitive...
Abstract: Data errors are a common challenge in machine learning (ML) projects and generally cause significant performance degradation in ML-enabled software systems. To ensure early detection of erroneous data and avoid training MLmodels using bad data, research and industrial practice suggest incorporating a data validation process and tool in the ML system development process. Aim: The study investigates...
Abstract: Quality requirements are vital to developing successful software products. However, there exist evidence that quality requirements are managed mostly in an “ad hoc” manner and down-prioritized. This may result in insecure, unstable, slow products, and unhappy customers. We have developed a conceptual model for the scoping process of quality requirements – QREME – and an assessment model – Q-REPM –...
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