Due to various trends in the automotive sector, such as autonomous driving and electrification, the number of Electric/Electronic (E/E) components has risen in both hardware and software. This has led to an increase in certification requirements, which cannot be fulfilled without simulation anymore [1]. Different approaches have emerged trying to master this issue. However, for supporting early design decisions in the E/E development, these are either domain-specific or too elaborate. In this paper, we demonstrate an approach to realize early design decisions through a cross-domain simulation of E/E architectures, regarding the environment, scenarios, vehicle physics, the scheduling of software components and the power supply net. We use static E/E architecture hardware models, consisting of Electronic Control Units (ECUs), sensors, actuators and the wiring harness, as the base for the structure of our simulation models. The individual E/E components are linked to parameterizable simulation model templates to facilitate scalable execution. Moreover, scenarios are used for model reduction and supply the simulation model with stimuli. The simulation model is synthesized in an automated manner. For the evaluation, we simulate the power consumption of an electric vehicle, dependent on different loads. It shows that considering hardware aspects in early design phases uncovers errors that would have been noticed much later, e.g. when using virtual Hardware In the Loop (vHIL) methods. We also investigate the scalability of our approach. As E/E architecture modeling tool, we use Vector PREEvision and for the simulation Mathworks Simulink.

During the development of large software-intensive systems, developers use several modeling languages and tools to describe a system from different viewpoints. Model-driven and view-based technologies have made it easier to define domain-specific languages and transformations. Nevertheless, using several languages leads to fragmentation of information, to redundancies in the system description, and eventually to inconsistencies. Inconsistencies have negative impacts on the system’s quality and are costly to fix. Often, there is no support for consistency management across multiple languages. Using a single language is no practicable solution either, as it is overly complex to define, use, and evolve such a language. View-based development is a suitable approach to deal with complex systems, and is widely used in other engineering disciplines. Still, we need to cope with the problems of fragmentation and consistency. In this paper, we present the Vitruvius approach for consistency in view-based modeling. We describe the approach by formalizing the notion of consistency, presenting languages for consistency preservation, and defining a model-driven development process. Furthermore, we show how existing models can be integrated. We have evaluated our approach at two case studies from component-based and embedded automotive software development, using our prototypical implementation based on the Eclipse Modeling Framework.

Models are used in software engineering to describe parts of a system that are relevant for the computation of specific analyses, or the provision of specific functionality. Metamodeling languages such as Ecore make it possible to realize analyses and functionality with model-driven technology, such as transformation engines. If models conform to a metamodel that was expressed using Ecore, numerous Eclipse-based tools can be reused to directly analyze, display, or transform models. In many software projects, models are, however, realized with objects of plain-old Java classes rather than an explicit metamodel, so these popular toolscannot be used.In this new ideas paper, we present an Ecoreification approach, which can be used to automatically extract Ecore-conforming metamodels from Java code, and a code generator that combines the benefits of both worlds. The resulting code can be used exactly as before, but it also uses the modeling infrastructure and implements all interfaces for Ecore-based tooling. This way, arbitrary non-standard models can be displayed and modified, for example using graphical Sirius editors, or transformed with well-proven transformation languages, such as QVT-O or ATL.

Despite the crucial importance of metamodeling for Model-Driven Engineering (MDE), there is still little discussion about the quality of metamodel design and its consequences in model-driven development processes. Presumably, the quality of metamodel design strongly affects the models and transformations that conform to these metamodels. However, so far surprisingly few work has been done to validate the characterization of metamodel quality. A proper characterization is essential to automate quality improvements for metamodels such as metamodel refactorings. In this paper, we present an empirical study to sharpen the understanding of the perception of metamodel quality. In the study, 24 participants created metamodels of two different domains and evaluated the metamodels in a peer review process according to an evaluation sheet. The results show that the perceived quality was mainly driven by the metamodels completeness, correctness and modularity while other quality attributes could be neglected.

To face continuously growing security threats and requirements, sound methodologies for constructing secure systems are required. In this context, Model-Driven Security (MDS) has emerged since more than a decade ago as a specialized Model-Driven Engineering approach for supporting the development of secure systems. MDS aims at improving the productivity of the development process and quality of the resulting secure systems, with models as the main artifact. This paper presents how we systematically examined existing published work in MDS and its results. The systematic review process, which is based on a formally designed review protocol, allowed us to identify, classify, and evaluate different MDS approaches. To be more specific, from thousands of relevant papers found, a final set of the most relevant MDS publications has been identified, strictly selected, and reviewed. We present a taxonomy for MDS, which is used to synthesize data in order to classify and evaluate the selected MDS approaches. The results draw a wide picture of existing MDS research showing the current status of the key aspects in MDS as well as the identified most relevant MDS approaches. We discuss the main limitations of the existing MDS approaches and suggest some potential research directions based on these insights.