Abstract
The size and the number of models is drastically increasing, preventing organizations from fully exploiting Model Driven Engineering benefits. Regarding this problem of scalability, some approaches claim to provide mechanisms that are adapted to numerous and huge models. The problem is that those approaches cannot be validated as it is not possible to obtain numerous and huge models and then to stress test them.
In this paper, we face this problem by proposing a uniform generator of huge models. Our approach is based on the Boltzmann method, whose two main advantages are its linear complexity which makes it possible to generate huge models, and its uniformity, which guarantees that the generation has no bias.
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Mougenot, A., Darrasse, A., Blanc, X., Soria, M. (2009). Uniform Random Generation of Huge Metamodel Instances. In: Paige, R.F., Hartman, A., Rensink, A. (eds) Model Driven Architecture - Foundations and Applications. ECMDA-FA 2009. Lecture Notes in Computer Science, vol 5562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02674-4_10
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DOI: https://doi.org/10.1007/978-3-642-02674-4_10
Publisher Name: Springer, Berlin, Heidelberg
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