Extensions and Scalability Experiments of a Generic Model-Driven Architecture for Variability Model Reasoning
Résumé
In this article, we overcome the limitations of this initial proposal in three key ways: (1) we add the ability to reason on textual or hybrid VMLs, rather than only on diagrammatic VMLs, enhancing the versatility of the architecture on the input side; (2) we enable the use of solvers from a third paradigm, enhancing the versatility of the architecture on the output side; and, (3) we present the results of scalability performance experiments of an implementation of this architecture. These results have been achieved without significantly altering the architecture, demonstrating its agnosticism with respect to specific VMLs and solvers. It also shows that it can underlie the implementation of practical variability reasoning tools that scale up to real sized variability model analysis and configuration needs.
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