Measuring Differences To Compare Sets Of Models And Improve Diversity In MDE

Adel Ferdjoukh 1, 2 Florian Galinier 3 Eric Bourreau 4 Annie Chateau 5 Clémentine Nebut 6
1 AtlanModels - Modeling Technologies for Software Production, Operation, and Evolution
Inria Rennes – Bretagne Atlantique , LS2N - Laboratoire des Sciences du Numérique de Nantes
4 MAORE - Méthodes Algorithmes pour l'Ordonnancement et les Réseaux
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
5 MAB - Méthodes et Algorithmes pour la Bioinformatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
6 MAREL - Models And Reuse Engineering, Languages
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Owning sets of models is crucial in many fields, so as to validate concepts or to test algorithms that handle models, model transformations. Since such models are not always available, generators can be used to automatically generate sets of models. Unfortunately, the generated models are very close to each others in term of graph structure and element naming is poorly diverse. Usually, they cover very badly the solutions' space. In this paper, we propose novel measures to estimate differences between two models and we provide solutions to handle a whole set of models and perform several operations on its models: comparing them, selecting the most diverse and representative and graphically view the diversity. Implementations presented in this paper are gathered in a tool named COMODI. We applied these model comparison measures in order to improve diversity in MDE using a genetic algorithm.
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Adel Ferdjoukh, Florian Galinier, Eric Bourreau, Annie Chateau, Clémentine Nebut. Measuring Differences To Compare Sets Of Models And Improve Diversity In MDE. ICSEA: International Conference on Software Engineering Advances, Oct 2017, Athenes, Greece. ⟨hal-01586827⟩

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