Abstract
We propose a genetic programming-based approach to automatically learn model transformation rules from prior transformation pairs of source-target models used as examples. Unlike current approaches, ours does not need fine-grained transformation traces to produce many-to-many rules. This makes it applicable to a wider spectrum of transformation problems. Since the learned rules are produced directly in an actual transformation language, they can be easily tested, improved and reused. The proposed approach was successfully evaluated on well-known transformation problems that highlight three modeling aspects: structure, time constraints, and nesting.
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Faunes, M., Sahraoui, H., Boukadoum, M. (2013). Genetic-Programming Approach to Learn Model Transformation Rules from Examples. In: Duddy, K., Kappel, G. (eds) Theory and Practice of Model Transformations. ICMT 2013. Lecture Notes in Computer Science, vol 7909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38883-5_2
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DOI: https://doi.org/10.1007/978-3-642-38883-5_2
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