Michael Franziskus Hönig
ABSTRACT
Applications for feature models, such as sampling, usually involve exploring a decision structure to systematically generate product configurations. This decision structure is often learned implicitly, using SAT solvers, or explicitly by describing it in the form of a binary decision diagram. Another structure, context-free grammars, have only been discussed for constraint-free feature models. We outline two algorithms that allow the transformation of feature models into context-free grammars and argue that, though those initial algorithms do not perform well, context-free grammars show promising potential for optimizations.
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Digital Library
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Index Terms
- Grammars for Feature Models
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