Semi-automated metamodel/model co-evolution: a multi-level interactive approach

Metamodels evolve even more frequently than programming languages. This evolution process may result in a large number of instance models that are no longer conforming to the revised metamodel. On the one hand, the manual adaptation of models after the metamodels' evolution can be tedious, error-prone, and time-consuming. On the other hand, the automated co-evolution of metamodels/models is challenging, especially when new semantics is introduced to the metamodels. While some interactive techniques have been proposed, designers still need to explore a large number of possible revised models, which makes the interaction time-consuming. Existing interactive tools are limited to interactions with the designers to evaluate the impact of the co-evolved models on different objectives of the number of inconsistencies, number of changes and the deviation from the initial models. However, designers are also interested to check the impact of introduced changes on the decision space which is composed by model elements. These interactions help designers to understand the differences of the co-evolved models solution that have similar objectives value to select the best one based on their preferences. In this paper, we propose an interactive approach that enables designers to select their preference simultaneously in the objective and decision spaces. Designers may be interested in looking at co-evolution operations that can improve a specific objective such as number of non-conformities with the revised metamodel (objective space), but such operations may be related to different model locations (decision space). A set of co-evolution solutions is generated at first using multi-objective search that suggests edit operations to designers based on three objectives: minimizing the deviation with the initial model, the number of non-conformities with the revised metamodel and the number of changes. Then, the approach proposes to the user few regions of interest by clustering the set of recommended co-evolution solutions of the multi-objective search. Also, another clustering algorithm is applied within each cluster of the objective space to identify solutions related to different model element locations. The objective and decision spaces can now be explored more efficiently by the designers, who can quickly select their preferred cluster and give feedback on a smaller number of solutions by eliminating similar ones. This feedback is then used to guide the search for the next iterations if the user is still not satisfied. We evaluated our approach on a set of metamodel/model co-evolution case studies and compared it to existing fully automated and interactive co-evolution techniques.