Abstract
Although a feature model can represent commonalities and variabilities in a very concise taxonomic form, features in a feature model are merely symbols. Mapping features to other models, such as behavioral or data specifications, gives them semantics. In this paper, we propose a general template-based approach for mapping feature models to concise representations of variability in different kinds of other models. We show how the approach can be applied to UML 2.0 activity and class models and describe a prototype implementation.
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Czarnecki, K., Antkiewicz, M. (2005). Mapping Features to Models: A Template Approach Based on Superimposed Variants. In: Glück, R., Lowry, M. (eds) Generative Programming and Component Engineering. GPCE 2005. Lecture Notes in Computer Science, vol 3676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11561347_28
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DOI: https://doi.org/10.1007/11561347_28
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