Tripel Graph Grammars (TGG) 3.0: A Framework for Reliable, Continuous Model Integration

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Model-Driven Engineering (MDE) is an established approach to manage the increasing complexity of engineering 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 settings, support for collaborative modelling processes on networks of models becomes more and more important. Collaborative modelling on networks of models is not yet mature enough to automatically detect and resolve inconsistencies and conflicts between model changes. Current MDE methods either allow only synchronous modelling 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 severe shortcomings in practical use, as model networks are often changed concurrently and model inconsistencies cannot always be resolved immediately. Existing approaches do not always scale sufficiently in practice or do not guarantee the correctness and completeness of computed model synchronizations. To strengthen the MDE vision for modelling in large projects, we aim to develop a framework for reliable, continuous model integration that provides a conceptual and technological basis for collaborative modelling processes across multiple application domains. This framework will support continuous integration of concurrent changes to models in a network while tolerating temporary model inconsistencies. Since Triple Graph Grammars (TGGs), a rule-based BX-approach, have proven to work well 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 in the first funding phase, our framework will support the development of networks of cooperating model integrators. Each model integrator is tasked with performing reliable, continuous model integration on a pair of connected models. To achieve this goal, each model integrator uses a Monitor-Analyze-Plan-Execute-cycle driven by a knowledge component (MAPE-K-cycle); a concept adopted from Self-X systems. For large-scale model integration, we consider networks of concurrently active model integrators. Our framework will be evaluated at Arcadia, a state-of-the-art methodology for model-based engineering in industry.