Feature-Level Change Impact Analysis Using Formal Concept Analysis

Abstract : Software Product Line Engineering (SPLE) is a system- atic reuse approach to develop a short time-to-market and quality products, called Software Product Line (SPL). Usu- ally, the SPL is not developed from scratch but it is devel- oped by reusing features (resp. their source code elements) of existing similar systems developed by ad-hoc reuse tech- niques. The feature implementations may be changed for adapting SPLE context. The change may impact other fea- tures that are not interested in the change, as a feature’s implementation spans multiple code elements and shares code elements with other features. Therefore, feature-level Change Impact Analysis (CIA) is needed to predict affected features for change management purpose. In this paper, we propose a feature-level CIA technique using formal concept analysis. In our experimental evaluation using three case studies of different domains and sizes, we show the effec- tiveness of our technique in terms of the most commonly used metrics on the subject.
Type de document :
Communication dans un congrès
SEKE: Software Engineering and Knowledge Engineering, Jul 2014, Vancouver, Canada. 26th International Conference on Software Engineering and Knowledge Engineering, 2014
Liste complète des métadonnées

Littérature citée [14 références]  Voir  Masquer  Télécharger

https://hal-lirmm.ccsd.cnrs.fr/lirmm-01291179
Contributeur : Abdelhak-Djamel Seriai <>
Soumis le : lundi 21 mars 2016 - 10:09:36
Dernière modification le : jeudi 11 janvier 2018 - 06:26:11
Document(s) archivé(s) le : dimanche 13 novembre 2016 - 20:57:08

Fichier

spl-seke14-1.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : lirmm-01291179, version 1

Collections

Citation

Hamzeh Eyal-Salman, Abdelhak-Djamel Seriai, Christophe Dony. Feature-Level Change Impact Analysis Using Formal Concept Analysis. SEKE: Software Engineering and Knowledge Engineering, Jul 2014, Vancouver, Canada. 26th International Conference on Software Engineering and Knowledge Engineering, 2014. 〈lirmm-01291179〉

Partager

Métriques

Consultations de la notice

50

Téléchargements de fichiers

80