Automating Variability Model Inference for Component-Based Language Implementations

Abstract : Recently, domain-specific language development has become again a topic of interest, as a means to help designing solutions to domain-specific problems. Componentized language frameworks, coupled with variability modeling, have the potential to bring language development to the masses, by simplifying the configuration of a new language from an existing set of reusable components. However, designing variability models for this purpose requires not only a good understanding of these frameworks and the way components interact, but also an adequate familiarity with the problem domain. In this paper we propose an approach to automatically infer a relevant variability model from a collection of already implemented language components, given a structured, but general representation of the domain. We describe techniques to assist users in achieving a better understanding of the relationships between language components, and find out which languages can be derived from them with respect to the given domain.
Type de document :
Communication dans un congrès
Patrick Heymans and Julia Rubin. SPLC'14 - 18th International Software Product Line Conference, Sep 2014, Florence, Italy. ACM, 2014
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Soumis le : mardi 15 juillet 2014 - 13:12:25
Dernière modification le : jeudi 9 février 2017 - 16:03:43
Document(s) archivé(s) le : vendredi 21 novembre 2014 - 17:21:14

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  • HAL Id : hal-01023864, version 1

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Edoardo Vacchi, Walter Cazzola, Benoit Combemale, Mathieu Acher. Automating Variability Model Inference for Component-Based Language Implementations. Patrick Heymans and Julia Rubin. SPLC'14 - 18th International Software Product Line Conference, Sep 2014, Florence, Italy. ACM, 2014. <hal-01023864>

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