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Astract: Smart production systems need to be able to adapt to changing environments and market needs. They have to reflect changes in (i) the reconfiguration of the production systems themselves, (ii) the processes they perform or (iii) the products they produce. Manual intervention for system adaptation is costly and potentially error-prone. In this article, we propose a model-driven approach for the automatic generation and regeneration of production plans that can be triggered anytime a change in any of the three aforementioned parameters occurs.

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Astract: AutomationML provides a versatile modeling environment for the description of production systems. However, when starting a new AutomationML project, or when serializing existing data with the AutomationML format, there are no rules on how to structure these models in a meaningful way. In this work, we present an approach for structuring AutomationML models, based on the IEC 62264 standard. In our approach we are implementing the process of serializing IEC 62264 information declaratively, by leveraging the power of model transformations, as they are applied in the context of model-driven (software) engineering.

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.

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Astract: Modern cloud computing environments support a relatively high degree of automation in service provisioning, which allows cloud service customers (CSC) to dynamically acquire services required for deploying cloud applications. Cloud modeling languages (CMLs) have been proposed to address the diversity of features provided by cloud computing environments and support different application scenarios, e.g., migrating existing applications to the cloud, developing new cloud applications, or optimizing them. There is, however, still much debate in the research community on what a CML is and what aspects of a cloud application and its target cloud computing environment should be modeled by a CML. Furthermore, the distinction between CMLs on a fine-grained level exposing their modeling concepts is rarely made. In this article, we investigate the diverse features currently provided by existing CMLs. We classify and compare them according to a common framework with the goal to support CSCs in selecting the CML which fits the needs of their application scenario and setting. As a result, not only features of existing CMLs are pointed out for which extensive support is already provided but also in which existing CMLs are deficient, thereby suggesting a research agenda.