Change Propagation-based and Composition-based Co-evolution of Transformations with Evolving Metamodels

Transformations constitute significant key components of an automated model-driven engineering solution. As metamodels evolve, model transformations may need to be co-evolved accordingly. A conducted experiment on transformations' co-evolution highlighted the existing gap in the literature where only limited few co-evolution scenarios are covered without supporting alternatives that occur in practice. To make matters worse, when a developer needs to drift apart from the proposed co-evolution, no automatic support is provided to the developer. This paper first proposes a change propagation-based co-evolution of transformations. The premise is that knowledge of the metamodel evolution can be propagated by means of resolutions to drive the transformation co-evolution. To deal with particular cases where developers must drift from the proposed resolutions, we introduce a composition-based mechanism that allows developers to compose resolutions meeting their needs. Our work is evaluated on 14 case studies consisting in original and evolved metamodels and ETL Epsilon transformations. A comparison of our co-evolved transformations with the 14 versioned ones showed the usefulness of our approach that reached an average 96% of correct co-evolution. On three other case studies, our composition-based co-evolution showed to be useful to eight developers in selecting resolutions that best meet their needs. Among the applied resolutions, four developers applied six resolutions that were the direct result of a composition.