In rule-based approaches, a model transformation definition tells how an instance of a source model should be transformed to an instance of a target model. As these models undergo changes, model transformations defined over these models may get out of sync. Restoring conformance between model transformations and the models is a complex and error prone task. In this paper, we propose a formal approach to automatically co-evolve model transformations according to the evolution of the models. The approach is based on encoding the model transformation definition as a traceability model and the evolution of the models as applications of graph transformation rules. These rules are used to obtain an evolved traceability model from the original traceability model. We will identify the criteria which need to be fulfilled in order to make this automatic co-evolution possible. We provide a tool support for this procedure, in which the evolved model transformation definition is derived from the evolved traceability model.
CITATION STYLE
Rutle, A., Iovino, L., König, H., & Diskin, Z. (2018). Automatic transformation co-evolution using traceability models and graph transformation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10890 LNCS, pp. 80–96). Springer Verlag. https://doi.org/10.1007/978-3-319-92997-2_6
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