A search-based and fault-tolerant approach to concurrent model synchronisation

In collaboration scenarios, we often encounter situations in which semantically interrelated models are changed concurrently. Concurrent model synchronization denotes the task of keeping these models consistent by propagating changes between them. This is challenging as changes can contradict each other and thus be in conflict. A problem with current synchronisation approaches is that they are often nondeterministic, i.e., the order in which changes are propagated is essential for the result. Furthermore, a common limitation is that the involved models must have been in a consistent state at some point, and that the applied changes are at least valid for the domain in which they were made. We propose a hybrid approach based on Triple Graph Grammars (TGGs) and Integer Linear Programming (ILP) to overcome these issues: TGGs are a grammar-based means that supplies us with a superset of possible synchronization solutions, forming a search space from which an optimum solution incorporating user-defined preferences can be chosen by ILP. Therefore, the proposed method combines configurability by comprising expert knowledge via TGGs with the flexible input handling of search-based techniques: By accepting arbitrary graph structures as input models, the approach is tolerant towards errors induced during the modelling process, i.e., it can cope with input models which do not conform to their metamodel or which cannot be generated by the TGG at hand. The approach is implemented in the model transformation tool eMoflon and evaluated regarding scalability for growing model sizes and an increasing number of changes.