Generating model transformation rules from examples using an evolutionary algorithm

Authors:
Martin Faunes;Houari Sahraoui;Mounir Boukadoum
Affiliations:
Université de Montréal, Canada;Université de Montréal, Canada;Université du Québec à Montréal, Canada
Venue:
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
Year:
2012

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Abstract

We propose an evolutionary approach to automatically generate model transformation rules from a set of examples. To this end, genetic programming is adapted to the problem of model transformation in the presence of complex input/output relationships (i.e., models conforming to meta-models) by generating declarative programs (i.e., transformation rules in this case). Our approach does not rely on prior transformation traces for the model-example pairs, and directly generates executable, many-to-many rules with complex conditions. The applicability of the approach is illustrated with the well-known problem of transforming UML class diagrams into relational schemas, using examples collected from the literature.