A precedence-driven approach for concurrent model synchronization scenarios using triple graph grammars

Concurrent model synchronization is the task of restoring consistency between two correlated models after they have been changed concurrently and independently. To determine whether such concurrent model changes conflict with each other and to resolve these conflicts taking domain- or user-specific preferences into account is highly challenging. In this paper, we present a framework for concurrent model synchronization algorithms based on Triple Graph Grammars (TGGs). TGGs specify the consistency of correlated models using grammar rules; these rules can be used to derive different consistency restoration operations. Using TGGs, we infer a causal dependency relation for model elements that enables us to detect conflicts non-invasively. Different kinds of conflicts are detected first and resolved by the subsequent conflict resolution process. Users configure the overall synchronization process by orchestrating the application of consistency restoration fragments according to several conflict resolution strategies to achieve individual synchronization goals. As proof of concept, we have implemented this framework in the model transformation tool eMoflon. Our initial evaluation shows that the runtime of our presented approach scales with the size of model changes and conflicts, rather than model size.