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 87i¾ź% and 89i¾ź%.

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