Titre :

Extending Feature Models with Relative Cardinalities

Auteur(s) :

Sousa, Gustavo [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Self-adaptation for distributed services and large software systems [SPIRALS]
Université de Lille, Sciences et Technologies
Rudametkin, Walter [Auteur]
Université de Lille, Sciences et Technologies
École polytechnique universitaire de Lille [Polytech Lille]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Self-adaptation for distributed services and large software systems [SPIRALS]
Duchien, Laurence [Auteur]
Université de Lille, Sciences et Technologies
Self-adaptation for distributed services and large software systems [SPIRALS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Titre de la manifestation scientifique :

20th International Systems and Software Product Line Conference

Ville :

Beijing

Pays :

Chine

Date de début de la manifestation scientifique :

2016-09-19

Titre de l’ouvrage :

Proceedings of the 20th International Systems and Software Product Line Conference (SPLC'16)

Date de publication :

2016-09-16

Mot(s)-clé(s) en anglais :

Cardinality
Constraints
Feature Model

Discipline(s) HAL :

Informatique [cs]
Informatique [cs]/Génie logiciel [cs.SE]

Résumé en anglais : [en]

Feature modeling is widely used to capture and manage common-alities and variabilities in software product lines. Cardinality-based feature models are used when variability applies not only to the selection or exclusion ...
Lire la suite >Feature modeling is widely used to capture and manage common-alities and variabilities in software product lines. Cardinality-based feature models are used when variability applies not only to the selection or exclusion of features but also to the number of times a feature can be included in a product. Feature cardinalities are usually considered to apply in either a local or global scope. However , we have identified that these interpretations are insufficient to capture the variability of cloud environments. In this paper, we redefine cardinality-based feature models to allow multiple relative cardinalities between features and we discuss the effects of relative cardinalities on feature modeling semantics, consistency and cross-tree constraints. To evaluate our approach we conducted an analysis of relative cardinalities in four cloud computing providers. In addition, we developed tools for reasoning on feature models with relative cardinalities and performed experiments to verify the performance and scalability of the approach. The results from our study indicate that extending feature models with relative cardinal-ities is feasible and improves variability modeling, particularly in the case of cloud environments.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL