Unfolding Cardinality-Based Feature Models

  • Context: Cardinality-based feature models (CFMs) are a generalization of boolean feature models with increased expressiveness. Analysis and sampling of CFMs are not available yet and are hard problems due to the inherent complexity of CFMs. However, for most CFMs, a translation to boolean FMs might be possible, which enables tools already available for "normal" FMs.


    Goal: An analysis of which CFMs can be translated into FMs, in which cases information loss may happen, and implement the translation in order to evaluate the algorithm.


    Requirements: Knowledge about (software) product lines is helpful, but not required. Interest in theoretical computer science is helpful.