Automated reasoning for derivation of model-driven SPLs

Model-Driven SPL approaches use metamodels and transformation rules to obtain concrete software artifacts departing from models. Most of such approaches use also feature models to express variability. Because of the variability, to derive products, they have to adapt the transformation rules accordi...

Full description

Autores:
Arboleda Jimenez, Hugo Fernando
Royer, Jean Claude
Díaz, Juan Francisco;
Vargas, Víctor;
Tipo de recurso:
http://purl.org/coar/resource_type/c_c94f
Fecha de publicación:
2010
Institución:
Universidad ICESI
Repositorio:
Repositorio ICESI
Idioma:
eng
OAI Identifier:
oai:repository.icesi.edu.co:10906/83099
Acceso en línea:
https://hal.inria.fr/hal-00536845/en
http://repository.icesi.edu.co/biblioteca_digital/handle/10906/83099
Palabra clave:
Modelos de sistemas
Arquitectura de software
Programación orientada a objetos (Computadores)
Ingeniería de sistemas y comunicaciones
Systems engineering
Systems Application Architecture
Hardware y arquitectura de computadores
Rights
openAccess
License
https://creativecommons.org/licenses/by-nc-nd/4.0/
Description
Summary:Model-Driven SPL approaches use metamodels and transformation rules to obtain concrete software artifacts departing from models. Most of such approaches use also feature models to express variability. Because of the variability, to derive products, they have to adapt the transformation rules according to user choices captured in feature configurations. In this paper we propose an approach based on Constraint Programming to derive Model-Driven SPLs. Our contribution is twofold. First, we assist product line architects when relating transformation rules and features in order to derive prod- ucts based on feature configurations; the novelty is that we facilitate the management of feature interactions to architects. Second, current approaches to reason on feature models in SPL Engineering only deal with problems related to product configuration. We improve such approaches adding facilities for product derivation.