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During the overall development of complex engineering systems different modeling notations are employed. For example, in the domain of automotive systems system engineering models are employed quite early to capture the requirements and basic structuring of the entire system, while software engineering models are used later on to describe the concrete software architecture. Each model helps in addressing the specific design issue with appropriate notations and at a suitable level of abstraction. However, when we step forward from system design to the software design, the engineers have to ensure that all decisions captured in the system design model are correctly transferred to the software engineering model. Even worse, when changes occur later on in either model, today the consistency has to be reestablished in a cumbersome manual step. In this report, we present in an extended version of [Holger Giese, Stefan Neumann, and Stephan Hildebrandt. Model Synchronization at Work: Keeping SysML and AUTOSAR Models Consistent. In Gregor Engels, Claus Lewerentz, Wilhelm Schäfer, Andy Schürr, and B. Westfechtel, editors, Graph Transformations and Model Driven Enginering - Essays Dedicated to Manfred Nagl on the Occasion of his 65th Birthday, volume 5765 of Lecture Notes in Computer Science, pages 555–579. Springer Berlin / Heidelberg, 2010.] how model synchronization and consistency rules can be applied to automate this task and ensure that the different models are kept consistent. We also introduce a general approach for model synchronization. Besides synchronization, the approach consists of tool adapters as well as consistency rules covering the overlap between the synchronized parts of a model and the rest. We present the model synchronization algorithm based on triple graph grammars in detail and further exemplify the general approach by means of a model synchronization solution between system engineering models in SysML and software engineering models in AUTOSAR which has been developed for an industrial partner. In the appendix as extension to [19] the meta-models and all TGG rules for the SysML to AUTOSAR model synchronization are documented.
The correctness of model transformations is a crucial element for the model-driven engineering of high quality software. A prerequisite to verify model transformations at the level of the model transformation specification is that an unambiguous formal semantics exists and that the employed implementation of the model transformation language adheres to this semantics. However, for existing relational model transformation approaches it is usually not really clear under which constraints particular implementations are really conform to the formal semantics. In this paper, we will bridge this gap for the formal semantics of triple graph grammars (TGG) and an existing efficient implementation. Whereas the formal semantics assumes backtracking and ignores non-determinism, practical implementations do not support backtracking, require rule sets that ensure determinism, and include further optimizations. Therefore, we capture how the considered TGG implementation realizes the transformation by means of operational rules, define required criteria and show conformance to the formal semantics if these criteria are fulfilled. We further outline how static analysis can be employed to guarantee these criteria.
The correctness of model transformations is a crucial element for model-driven engineering of high-quality software. A prerequisite to verify model transformations at the level of the model transformation specification is that an unambiguous formal semantics exists and that the implementation of the model transformation language adheres to this semantics. However, for existing relational model transformation approaches, it is usually not really clear under which constraints particular implementations really conform to the formal semantics. In this paper, we will bridge this gap for the formal semantics of triple graph grammars (TGG) and an existing efficient implementation. While the formal semantics assumes backtracking and ignores non-determinism, practical implementations do not support backtracking, require rule sets that ensure determinism, and include further optimizations. Therefore, we capture how the considered TGG implementation realizes the transformation by means of operational rules, define required criteria, and show conformance to the formal semantics if these criteria are fulfilled. We further outline how static and runtime checks can be employed to guarantee these criteria.
Model-driven software development requires techniques to consistently propagate modifications between different related models to realize its full potential. For large-scale models, efficiency is essential in this respect. In this paper, we present an improved model synchronization algorithm based on triple graph grammars that is highly efficient and, therefore, can also synchronize large-scale models sufficiently fast. We can show, that the overall algorithm has optimal complexity if it is dominating the rule matching and further present extensive measurements that show the efficiency of the presented model transformation and synchronization technique.
Advanced mechatronic systems have to integrate existing technologies from mechanical, electrical and software engineering. They must be able to adapt their structure and behavior at runtime by reconfiguration to react flexibly to changes in the environment. Therefore, a tight integration of structural and behavioral models of the different domains is required. This integration results in complex reconfigurable hybrid systems, the execution logic of which cannot be addressed directly with existing standard modeling, simulation, and code-generation techniques. We present in this paper how our component-based approach for reconfigurable mechatronic systems, M ECHATRONIC UML, efficiently handles the complex interplay of discrete behavior and continuous behavior in a modular manner. In addition, its extension to even more flexible reconfiguration cases is presented.
Advanced mechatronic systems have to integrate existing technologies from mechanical, electrical and software engineering. They must be able to adapt their structure and behavior at runtime by reconfiguration to react flexibly to changes in the environment. Therefore, a tight integration of structural and behavioral models of the different domains is required. This integration results in complex reconfigurable hybrid systems, the execution logic of which cannot be addressed directly with existing standard modeling, simulation, and code-generation techniques. We present in this paper how our component-based approach for reconfigurable mechatronic systems, MECHATRONIC UML, efficiently handles the complex interplay of discrete behavior and continuous behavior in a modular manner. In addition, its extension to even more flexible reconfiguration cases is presented.
Currently, a transformation of our technical world into a networked technical world where besides the embedded systems with their interaction with the physical world the interconnection of these nodes in the cyber world becomes a reality can be observed. In parallel nowadays there is a strong trend to employ artificial intelligence techniques and in particular machine learning to make software behave smart. Often cyber-physical systems must be self-adaptive at the level of the individual systems to operate as elements in open, dynamic, and deviating overall structures and to adapt to open and dynamic contexts while being developed, operated, evolved, and governed independently.
In this presentation, we will first discuss the envisioned future scenarios for cyber-physical systems with an emphasis on the synergies networking can offer and then characterize which challenges for the design, production, and operation of these systems result. We will then discuss to what extent our current capabilities, in particular concerning software engineering match these challenges and where substantial improvements for the software engineering are crucial. In today's software engineering for embedded systems models are used to plan systems upfront to maximize envisioned properties on the one hand and minimize cost on the other hand. When applying the same ideas to software for smart cyber-physical systems, it soon turned out that for these systems often somehow more subtle links between the involved models and the requirements, users, and environment exist. Self-adaptation and runtime models have been advocated as concepts to covers the demands that result from these subtler links. Lately, both trends have been brought together more thoroughly by the notion of self-aware computing systems. We will review the underlying causes, discuss some our work in this direction, and outline related open challenges and potential for future approaches to software engineering for smart cyber-physical systems.
The service-oriented architecture supports the dynamic assembly and runtime reconfiguration of complex open IT landscapes by means of runtime binding of service contracts, launching of new components and termination of outdated ones. Furthermore, the evolution of these IT landscapes is not restricted to exchanging components with other ones using the same service contracts, as new services contracts can be added as well. However, current approaches for modeling and verification of service-oriented architectures do not support these important capabilities to their full extend.In this report we present an extension of the current OMG proposal for service modeling with UML - SoaML - which overcomes these limitations. It permits modeling services and their service contracts at different levels of abstraction, provides a formal semantics for all modeling concepts, and enables verifying critical properties. Our compositional and incremental verification approach allows for complex properties including communication parameters and time and covers besides the dynamic binding of service contracts and the replacement of components also the evolution of the systems by means of new service contracts. The modeling as well as verification capabilities of the presented approach are demonstrated by means of a supply chain example and the verification results of a first prototype are shown.
In this extended abstract, we will analyze the current challenges for the envisioned Self-Adaptive CPS. In addition, we will outline our results to approach these challenges with SMARTSOS [10] a generic approach based on extensions of graph transformation systems employing open and adaptive collaborations and models at runtime for trustworthy self-adaptation, self-organization, and evolution of the individual systems and the system-of-systems level taking the independent development, operation, management, and evolution of these systems into account.
Zum Thema "Quo vadis, Modellierung?" hält Prof. Dr. Holger Giese am 11. Dezember 2008 seine Antrittsvorlesung an der Universität Potsdam. Der Wissenschaftler bekleidet eine Professur für Systemanalyse und Modellierung. Es handelt sich um eine gemeinsame Berufung der Universität Potsdam mit dem Hasso-Plattner- Institut für Softwaresystemtechnik an der Universität Potsdam. Seit den Anfängen der Informatik vollzieht sich die Entwicklung von detaillierten, lösungsorientierten und eher technisch geprägten Modellen hin zu solchen, die immer abstrakter und eher an den Problemen beziehungsweise Anwendungsbereichen orientiert sind. Diese ermöglichen es, die Komplexität heutiger Systeme besser zu beherrschen. Der Einsatz führt in einigen Anwendungsbereichen heute schon zu bedeutend höherer Produktivität und Qualität sowie geringeren Entwicklungszeiten. Anderseits hat sich aber auch in anderen Anwendungsgebieten gezeigt, dass die ständige Anpassung der Software an sich ändernde Anforderungen oder Organisationsstrukturen dazu führt, dass in frühen Entwicklungsphasen entstandene Modelle in der Praxis oft sehr schnell nicht mehr mit der Software übereinstimmen. In seiner Antrittsvorlesung will Holger Giese diese Entwicklung Revue passieren lassen und der Frage nachgehen, was dies für die Zukunft der Modellierung bedeutet, mit welchen aktuellen Ansätzen man diesem Problem zu begegnen versucht und welche zukünftigen Entwicklungen für die Modellierung zu erwarten sind.
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