Learning transformation rules from transformation examples: An approach based on Relational Concept Analysis

Xavier Dolques 1 Marianne Huchard 2 Clémentine Nebut 2 Philippe Reitz 2
1 LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 MAREL - Models And Reuse Engineering, Languages
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : In Model Driven Engineering (MDE), model transformations are basic and primordial entities, thus easing their design and implementation is an important issue. A quite recently proposed way to create model transformations consists in deducing a transformation from examples of transformed models. Examples are easier to write than a transformation program and are often already available. We propose in this paper a method based on a machine learning method of the lattice domain, the Relational Concept Analysis, and an implementation of this method.
Keywords : Model Driven Engineering Model transformation Model transformation generation Formal Concept Analysis Relational Concept Analysis
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Communication dans un congrès
G. Guizzardi; L. Kutnoven. EDOC: Enterprise Distributed Object Computing Conference, Oct 2010, Vittoria, Brazil. IEEE Computer Society, 14th International Enterprise Distributed Object Computing Conference, pp.27-32, 2010, 〈http://edoc2010.inf.ufes.br/〉. 〈10.1109/EDOCW.2010.32〉
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Contributeur : Marianne Huchard <>
Soumis le : vendredi 5 novembre 2010 - 18:30:27
Dernière modification le : vendredi 20 juillet 2018 - 19:58:02
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Xavier Dolques, Marianne Huchard, Clémentine Nebut, Philippe Reitz. Learning transformation rules from transformation examples: An approach based on Relational Concept Analysis. G. Guizzardi; L. Kutnoven. EDOC: Enterprise Distributed Object Computing Conference, Oct 2010, Vittoria, Brazil. IEEE Computer Society, 14th International Enterprise Distributed Object Computing Conference, pp.27-32, 2010, 〈http://edoc2010.inf.ufes.br/〉. 〈10.1109/EDOCW.2010.32〉. 〈lirmm-00533375〉

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