Configuration of Cardinality-Based Feature Models Using Generative Constraint Satisfaction

Abstract

Existing feature modeling approaches and tools are based on classical constraint satisfaction which consists of a fixed set of variables and a fixed set of constraints on these variables. In many applications however, features may not only be selected but cloned so that the numbers of involved variables and constraints are not known from the beginning. We present a novel configuration approach for corresponding cardinality-based feature models based on the formalism of generative constraint satisfaction which - in extension to many existing approaches - is able to handle constraints in the context of multiple (cloned) features (e.g., by automatically creating new feature clones on the fly).