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Easing Model Transformation Learning with Automatically Aligned Examples

Abstract : Model Based Transformation Example (MTBE) is a recent track of research aiming at learning a transformation from examples. In most MTBE processes, a transformation example is given in the form of a source model, a transformed model and links between source elements and the corresponding transformed elements. Building the links is done manually, which is a tedious task, while in many cases, they can be de- duced from the examination of the source and transformed models, by using relevant attributes, like names or identifiers. We exploit this characteristic by proposing a semi-automatic matching operation, suitable for discovering matches between the source model and the transformed model. Our technique is inspired by and extends the Anchor-Prompt approach, and is based on the automatic discovery of pairs of anchors (pairs of elements for which there is a strong assumption of matching) to support the whole matching discovery. An implementation of the approach is provided for validation on a case study.
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Submitted on : Sunday, August 21, 2011 - 2:12:20 PM
Last modification on : Friday, October 22, 2021 - 3:07:25 PM
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Xavier Dolques, Aymen Dogui, Jean-Rémy Falleri, Marianne Huchard, Clémentine Nebut, et al.. Easing Model Transformation Learning with Automatically Aligned Examples. ECMFA'11: 7th European Conference Modelling - Foundation and Applications, Jun 2011, Birmingham, United Kingdom. pp.189-204, ⟨10.1007/978-3-642-21470-7_14⟩. ⟨lirmm-00616271⟩



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