Model-Driven Generation of Context-Specific Feature Models

Thibaut Possompès 1, * Christophe Dony 1 Marianne Huchard 1 Chouki Tibermacine 1
* Auteur correspondant
1 MAREL - Models And Reuse Engineering, Languages
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Software Product Lines (SPL) aim at deriving software architectures or systems from a software artifact base. Configuring the SPL to derive a new product is now usually done by selecting appropriate software features in a kind of models, called feature models. In some situations, a feature represents a software artifact associated to an element e of a context the software product will manage. Such a feature and its associated software artifact may be cloned according to the number of occurrences of e in the context and constraints have to be respected. Hence, the feature model proposed to users for configuration has to be adapted in a new dedicated phase according to the context elements. We propose a model-driven engineering approach for transforming a generic feature model according to a context model that a derived software product will manage. More precisely this paper describes an original model transformation able to generate context specific feature models including duplicated features, and removing inappropriate features. Our transformation is validated on a smart building optimization software case study.
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Communication dans un congrès
SEKE: Software Engineering and Knowledge Engineering, Jun 2013, Boston, United States. 25th International Conference on Software Engineering & Knowledge Engineering, pp.250-255, 2013, 〈http://www.ksi.edu/seke/seke13.html〉
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Thibaut Possompès, Christophe Dony, Marianne Huchard, Chouki Tibermacine. Model-Driven Generation of Context-Specific Feature Models. SEKE: Software Engineering and Knowledge Engineering, Jun 2013, Boston, United States. 25th International Conference on Software Engineering & Knowledge Engineering, pp.250-255, 2013, 〈http://www.ksi.edu/seke/seke13.html〉. 〈lirmm-00918263〉

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