Heuristic-Based Recommendation for Metamodel — OCL Coevolution

Abstract

We propose a novel approach for solving the problem of coevolution between metamodels and OCL constraints. Unlike existing solutions, our approach does not rely on predefined update rules and explicit tracking of high level changes to the metamodel. Rather, we pose it as a multi-objective optimization problem, exploring the space of possible OCL modifications to identify solutions that (a) do not violate the structure of the new version of the metamodel, (b) minimize changes to existing constraints, and (c) minimize loss of information. Finally, we recommend an appropriate subset of solutions to the user. We evaluate our approach on three cases of metamodel and OCL coevolution. The results show that we recommend accurate solutions for updating OCL constraints, even for complex evolution changes.