Automated Metamodel/Model Co-Evolution using a Multi-Objective Optimization Approach

Metamodels undergo many changes during the evolution of several software modeling languages and projects. As a consequence, models have to be updated for preserving their conformance with the new metamodel versions. A common practice is to manually define rules for each metamodel evolution to co-evolve the corresponding models. In this paper, we propose a generic automated approach for the metamodel/model coevolution. In our approach, we view the co-evolution as a multiobjective optimization problem, and we solve it using the NSGAII algorithm. Our algorithm search for solutions that minimize (1) the non-conformities with the new metamodel version, (2) the changes to the existing models, and (3) the loss of information. We successfully evaluated our approach on the evolution of the well-known UML state machine metamodel.