Reverse Engineering Language Product Lines from Existing DSL Variants - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Journal of Systems and Software Année : 2017

Reverse Engineering Language Product Lines from Existing DSL Variants

Résumé

The use of domain-specific languages (DSLs) has become a successful technique to develop complex systems. In this context, an emerging phenomenon is the existence of DSL variants, which are different versions of a DSL adapted to specific purposes but that still share commonalities. In such a case, the challenge for language designers is to reuse, as much as possible, previously defined language constructs to narrow implementation from scratch. To overcome this challenge, recent research in software languages engineering introduced the the notion of language product lines. Similarly to software product lines, language product lines are often built from a set of existing DSL variants. In this article, we propose a reverse-engineering technique to ease-off such a development scenario. Our approach receives a set of DSL variants which are used to automatically recover a language modular design and to synthesize the corresponding variability models. The validation is performed in a project involving industrial partners that required three different variants of a DSL for finite state machines. This validation shows that our approach is able to correctly identify commonalities and variability.
Fichier principal
Vignette du fichier
elsarticle-template.pdf (3.2 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01524632 , version 1 (18-05-2017)

Identifiants

Citer

David A Méndez-Acuña, José A Galindo, Benoit Combemale, Arnaud Blouin, Benoit Baudry. Reverse Engineering Language Product Lines from Existing DSL Variants. Journal of Systems and Software, 2017, ⟨10.1016/j.jss.2017.05.042⟩. ⟨hal-01524632⟩
396 Consultations
443 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More