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Triple Graph Grammars (TGG) 3.0: A Framework for Reliable, continuous model integration
Funded by the German Research Foundation (2023-2026).
Model-Driven Engineering (MDE) is an established approach to manage the increasing complexity of technical products. Models help to capture the essence of product developments. As engineering projects tend to become more complex and development teams increasingly work in distributed environments, support for collaborative modeling processes on networks of models becomes more and more important. Collaborative modeling in model networks is not yet mature enough to automatically identify and eliminate inconsistencies and conflicts between model changes. Current MDE methods either allow only synchronous modeling activities, provide a "team variant" with pessimistic locking at the model element level, or allow only limited possibilities for concurrent editing of model pairs. Bidirectional transformations (BX) promise to greatly simplify the development of model synchronization tasks. While BX approaches are mature for basic model synchronization processes on model pairs, they still have serious shortcomings in practical use, as model networks are often changed concurrently and inconsistencies in models cannot always be resolved immediately. Existing approaches do not always scale sufficiently in practice or guarantee the correctness and completeness of the calculated model synchronizations.
To strengthen the MDE vision for modeling in large-scale projects, we will develop a framework for reliable and continuous model integration that will provide a conceptual and technological basis for collaborative modeling processes across multiple application domains. This framework will support the continuous integration of concurrent changes to models of a network and tolerate temporary model inconsistencies. As triple graph grammars (TGGs), a rule-based approach to BX, have proven themselves in practice and have a comprehensive formal foundation, we will develop the framework in the context of TGGs. Based on improved model synchronization methods and tools for TGGs that we developed during the first phase of funding, our framework will support the development of networks of cooperating model integrators. Each model integrator has the task of performing a reliable, continuous model integration for a pair of models. To achieve this goal, each model integrator uses a monitor-analyze-plan-execute cycle which is controlled by a knowledge component (MAPE-K cycle); a concept adopted from Self-X systems. For the model integration on a large scale, we consider networks of simultaneously active model integrators. Our framework is being evaluated on Arcadia, a current methodology for model-based development in industry. development in industry.
Cooperation partners
Prof. Dr. Andy Schürr (TU Darmstadt)
Project collaborators
Lars Fritsche (TU Darmstadt)
Alexander Lauer (Philipps-Universität Marburg)