No abstract

Model transformations are touted to play a key role in Model Driven Developmente. Although well-established standards for creating metamodels such as the Meta-Object Facility exist, there is currently no mature foundation for specifying transformations among models. We propose a framework for the classification of several existing and proposed model transformation approaches. The classification framework is given as a feature model that makes explicit the different design choices for model transformations. Based on our analysis of model transformation approaches, we propose a few major categories in which most approaches fit.

No abstract

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.

Feature modeling is an important approach to capture the commonalities and variabilities in system families and product lines. Cardinality‐based feature modeling integrates a number of existing extensions of the original feature‐modeling notation from Feature‐Oriented Domain Analysis. Staged configuration is a process that allows the incremental configuration of cardinality‐based feature models. It can be achieved by performing a step‐wise specialization of the feature model. In this article, we argue that cardinality‐based feature models can be interpreted as a special class of context‐free grammars. We make this precise by specifying a translation from a feature model into a context‐free grammar. Consequently, we provide a semantic interpretation for cardinality‐based feature models by assigning an appropriate semantics to the language recognized by the corresponding grammar. Finally, we give an account on how feature model specialization can be formalized as transformations on the grammar equivalent of feature models. Copyright © 2005 John Wiley & Sons, Ltd.