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.
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Submitted on : Tuesday, July 15, 2014 - 1:12:25 PM
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Edoardo Vacchi, Walter Cazzola, Benoit Combemale, Mathieu Acher. Automating Variability Model Inference for Component-Based Language Implementations. SPLC'14 - 18th International Software Product Line Conference, Sep 2014, Florence, Italy. ⟨hal-01023864⟩

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