Learning Model Transformation Patterns using Graph Generalization

Hajer Saada 1 Marianne Huchard 1 Michel Liquière 2 Clémentine Nebut 1
1 MAREL - Models And Reuse Engineering, Languages
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 COCONUT - Agents, Apprentissage, Contraintes
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : In Model Driven Engineering (MDE), a Model Transforma-tion is a specialized program, often composed of a set of rules to transform models. The Model Transformation By Example (MTBE) approach aims to assist the developer by learning model transformations from source and target model examples.In a previous work, we proposed an approach which takes as input a fragmented source model and a target model, and produces a set of fragment pairs that presents the many-to-many match-ing links between the two models. In this paper, we propose to mine model transformation patterns (that can be later transformed in trans-formation rules) from the obtained matching links. We encode our models into labeled graphs that are then classified using the GRAAL approach to get meaningful common subgraphs. New transformation patterns are then found from the classification of the matching links based on their graph ends. We evaluate the feasibility of our approach on two represen-tative small transformation examples.
Keywords : Model transformation Formal Concept Analysis Galois lattice Model-Driven Engineering
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Conference papers
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Hajer Saada, Marianne Huchard, Michel Liquière, Clémentine Nebut. Learning Model Transformation Patterns using Graph Generalization. CLA: Concept Lattices and their Applications, Institute of Computer Science, Pavol Jozef Šafárik University in Košice, Ondrej Krídlo, Oct 2014, Košice, Slovakia. pp.11-22. ⟨hal-01075523⟩

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