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Many variability modeling tasks can be supported by automated analyses of models. Unfortunately, most analyses for Boolean variability models are NP-hard, while analyses for non-Boolean models easily become undecidable. It is thus crucial to exploit the properties of realistic models to construct viable analysis algorithms. Unfortunately, little work exists about non-Boolean models, and no benchmarks are available for such.
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Passos et al. (2011) studied this question.
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