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Automated Verification of Model Transformations in the Automotive Industry

  • Conference paper
Model-Driven Engineering Languages and Systems (MODELS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8107))

Abstract

Many companies have adopted MDD for developing their software systems. Several studies have reported on such industrial experiences by discussing the effects of MDD and the issues that still need to be addressed. However, only a few studies have discussed using automated verification of industrial model transformations. We previously demonstrated how transformations can be used to migrate GM legacy models to AUTOSAR models. In this study, we investigate using automated verification for such industrial transformations. We report on applying an automated verification approach to the GM-to-AUTOSAR transformation that is based on checking the satisfiability of a relational transformation representation, or a transformation model, with respect to well-formedness OCL constraints. An implementation of this approach is available as a prototype for the ATL language. We present the verification results of this transformation and discuss the practicality of using such tools on industrial size problems.

This work was partially funded by the Nouvelles Équipes Program of the Pays de la Loire Region (France), and by NSERC (Canada), as part of the NECSIS Automotive Partnership with General Motors, IBM Canada and Malina Software Corp.

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Selim, G.M.K., Büttner, F., Cordy, J.R., Dingel, J., Wang, S. (2013). Automated Verification of Model Transformations in the Automotive Industry. In: Moreira, A., Schätz, B., Gray, J., Vallecillo, A., Clarke, P. (eds) Model-Driven Engineering Languages and Systems. MODELS 2013. Lecture Notes in Computer Science, vol 8107. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41533-3_42

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  • DOI: https://doi.org/10.1007/978-3-642-41533-3_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41532-6

  • Online ISBN: 978-3-642-41533-3

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