Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
Genetic-Programming Approach to Learn Model Transformation Rules from Examples
verfasst von : Martin Faunes, Houari Sahraoui, Mounir Boukadoum
Erschienen in: Theory and Practice of Model Transformations
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.