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Genetic-Programming Approach to Learn Model Transformation Rules from Examples

  • Martin Faunes
  • Houari Sahraoui
  • Mounir Boukadoum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7909)

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.

Keywords

Genetic Programming Source Model Model Transformation Transformation Rule Target Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Martin Faunes
    • 1
  • Houari Sahraoui
    • 1
  • Mounir Boukadoum
    • 2
  1. 1.DIROUniversité de MontréalCanada
  2. 2.Université du Québec à MontréalCanada

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