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 exis ting set of reusable components. However, designing variability models for this purpose requires not only a good understanding of these frameworks and the way components inter act, 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.
Automating Variability Model Inference for Component-Based Language Implementations / E. Vacchi, W. Cazzola, B. Combemale, M. Acher - In: Proceedings of the 18th International Software Product Line Conference (SPLC'14)[s.l] : ACM, 2014 Sep. - pp. 167-176 (( Intervento presentato al 18. convegno Software Product Line Conference tenutosi a Firenze nel 2014 [10.1145/2648511.2648529].
Automating Variability Model Inference for Component-Based Language Implementations
E. VacchiPrimo
;W. CazzolaSecondo
;
2014
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 exis ting set of reusable components. However, designing variability models for this purpose requires not only a good understanding of these frameworks and the way components inter act, 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.Pubblicazioni consigliate
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