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Metamodel Matching Techniques: Review, Comparison and Evaluation

Metamodel Matching Techniques: Review, Comparison and Evaluation

Lamine Lafi, Jamel Feki, Slimane Hammoudi
Copyright: © 2014 |Volume: 5 |Issue: 2 |Pages: 25
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781466654969|DOI: 10.4018/ijismd.2014040104
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MLA

Lafi, Lamine, et al. "Metamodel Matching Techniques: Review, Comparison and Evaluation." IJISMD vol.5, no.2 2014: pp.70-94. http://doi.org/10.4018/ijismd.2014040104

APA

Lafi, L., Feki, J., & Hammoudi, S. (2014). Metamodel Matching Techniques: Review, Comparison and Evaluation. International Journal of Information System Modeling and Design (IJISMD), 5(2), 70-94. http://doi.org/10.4018/ijismd.2014040104

Chicago

Lafi, Lamine, Jamel Feki, and Slimane Hammoudi. "Metamodel Matching Techniques: Review, Comparison and Evaluation," International Journal of Information System Modeling and Design (IJISMD) 5, no.2: 70-94. http://doi.org/10.4018/ijismd.2014040104

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Abstract

During the last decade, Model Driven Engineering (MDE) has been proposed for supporting the development, maintenance and evolution of software systems. Model Driven Architecture (MDA), Software Factories and Eclipse Modeling Framework (EMF) are among the most representatives MDE approaches. Nowadays, it is well recognized that model transformation is at the heart of MDE approaches and, consequently represents one of the most important operations in MDE. However, despite the multitude of model transformation language proposals emerging from academic world and industry, these transformations are often manually specified; which is a tedious and error-prone task, and therefore an expensive process. Matching operation between metamodels is the keystone toward a (semi-)automatic transformation process. In this paper, the authors review metamodel matching techniques of the literature and then analyze their pros and cons in order to show how they can be useful for a semi-automatic transformation process. The result is a comparison of metamodel matching techniques, highlighting their similarities and differences in terms of information used for matching, demonstrating significant similarities between these techniques. Next, the authors compare four well-known metamodel matching techniques namely Similarity flooding, SAMT4MDE+ (extended Semi-Automatic Matching Tool for Model Driven Engineering), ModelCVS and AML (AtlanMod Matching Language) on ten couples of metamodels. For this comparison, the authors define a set of six criteria inspired from the database schema matching. One among these criteria is relevant to the quality of matching and for which we define a quality measure metrics. Furthermore, the authors develop a plug-in under Eclipse to support our comparison using ten couples of metamodels.

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