Automated Metamodel/Model Co-evolution Using a Multi-objective Optimization Approach

We propose a generic automated approach for the metamodel/model co-evolution. The proposed technique refines an initial model to make it as conformant as possible to the new metamodel version by finding the best compromise between three objectives, namely minimizing (i) the non-conformities with new metamodel version, (ii) the changes to existing models, and (iii) the loss of information. Consequently, we view the co-evolution as a multi-objective optimization problem, and solve it using the NSGA-II algorithm. We successfully validated our approach on the evolution of the well-known UML state machine metamodel. The results confirm the effectiveness of our approach with average precision and recall respectively higher than 87% and 89%.