Variational systems are ubiquitous in many application areas today. They use features to control presence and absence of system functionality. One challenge in the development of variational systems is their formal analysis and verification. Researchers have addressed this problem by designing aggregate so-called family-based verification algorithms. Family-based model checking allows simultaneous verification of all variants of a system family (variational system) in a single run by exploiting the commonalities between the variants. Yet, the computational cost of family-based model checking still greatly depends on the number of variants. In order to make it computationally cheaper, we can use variability abstractions for deriving abstract family-based model checking, where the variational model of a system family is replaced with an abstract (smaller) version of it which preserves the satisfaction of LTL properties. The variability abstractions can be combined with different partitionings of the set of variants to infer various verification scenarios for the variational model. However, manually finding an optimal verification scenario is hard since it requires a good knowledge of the family and property, while the number of possible scenarios is very large. In this work, we present an automatic iterative abstraction refinement procedure for family-based model checking. We use Craig interpolation to refine abstract variational models based on the obtained spurious counterexamples (traces). The refinement procedure works until a genuine counterexample is found or the property satisfaction is shown for all variants in the family. We illustrate the practicality of this approach for several variational benchmark models.
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