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Astract: Although Model-Based Software Engineering (MBE) is a widely accepted Software Engineering (SE) discipline, no agreed-upon core set of concepts and practices (i.e., a Body of Knowledge) has been defined for it yet. With the goals of characterizing the contents of the MBE discipline, promoting a global consistent view of it, clarifying its scope with regard to other SE disciplines, and defining a foundation for the development of educational curricula on MBE, this paper proposes the contents for a Body of Knowledge for MBE. We also describe the methodology that we have used to come up with the proposed list of contents, as well as the results of a survey study that we conducted to sound out the opinion of the community on the importance of the proposed topics and their level of coverage in the existing SE curricula.

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Astract: The CPS/IoT Ecosystem project aims to build an IoT infrastructure that will be used as a platform for research and education in multiple disciplines related to CPS and IoT. The main objective is to provide a real-world infrastructure, and allow students and researchers explore its capabilities on actual use cases.

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Astract: Current trends in manufacturing lead to more intelligent products, produced in global supply chains in shorter cycles, taking more and complex requirements into account. To manage this increasing complexity, cognitive decision support systems, building on data analytic approaches and focusing on the product life cycle, stages seem a promising approach. With two high-tech companies (world market leader in their domains) from Austria, we are approaching this challenge and jointly develop cognitive decision support systems for three real world industrial use cases. Within this position paper, we introduce our understanding of cognitive decision support and we introduce three industrial use cases, focusing on the requirements for cognitive decision support. Finally, we describe our preliminary solution approach for each use case and our next steps

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Astract: IoT systems are becoming an increasingly important component of the civil and industrial infrastructure. With the growth of these IoT ecosystems, their complexity is also growing exponentially. In this paper we explore the problem of testing and evaluating large scale IoT systems at design time. To this end we employ simulated sensors with the physical and geographical characteristics of real sensors. Moreover, we propose Sensyml, a simulation environment that is capable of generating big data from cyber-physical models and real-world data. To the best of our knowledge it is the first approach to use a hybrid integration of real and simulated sensor data, that is also capable of being integrated into existing IoT systems. Sensyml is a cloud based Infrastructure-as-a-Service (IaaS) system that enables users to test both functionality and scalability of their IoT applications.

Keywords: Java annotations, UML profiles, Model-based software engineering, Forward engineering, Reverse engineering
Astract: The capability of UML profiles to serve as annotation mechanism has been recognized in both research and industry. Today’s modeling tools offer profiles specific to platforms, such as Java, as they facilitate model-based engineering approaches. However, considering the large number of possible annotations in Java, manually developing the corresponding profiles would only be achievable by huge development and maintenance efforts. Thus, leveraging annotation-based modeling requires an automated approach capable of generating platform-specific profiles from Java libraries. To address this challenge, we present the fully automated transformation chain realized by Jump, thereby continuing existing mapping efforts between Java and UML by emphasizing on annotations and profiles. The evaluation of Jump shows that it scales for large Java libraries and generates profiles of equal or even improved quality compared to profiles currently used in practice. Furthermore, we demonstrate the practical value of Jump by contributing profiles that facilitate reverse engineering and forward engineering processes for the Java platform by applying it to a modernization scenario.