Fault localization using itemset mining under constraints

Mehdi Maamar 1 Nadjib Lazaar 2 Samir Loudni 3 Yahia Lebbah 1
2 COCONUT - Agents, Apprentissage, Contraintes
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : We introduce in this paper an itemset mining approach to tackle the fault localization problem, which is one of the most difficult processes in software debug- ging. We formalize the problem of fault localization as finding the k best patterns satisfying a set of constraints modelling the most suspicious statements. We use a Constraint Programming (CP) approach to model and to solve our itemset based fault localization problem. Our approach consists of two steps: (i) mining top-k suspicious suites of statements; (ii) fault localization by processing top-k patterns. Experiments performed on standard benchmark programs show that our approach enables to pro- pose a more precise localization than a standard approach
Type de document :
Article dans une revue
Automated Software Engineering, Springer Verlag, 2017, 24 (2), pp.341-368. 〈10.1007/s10515-015-0189-z〉
Liste complète des métadonnées

https://hal-lirmm.ccsd.cnrs.fr/lirmm-01276633
Contributeur : Joël Quinqueton <>
Soumis le : mercredi 17 octobre 2018 - 11:21:48
Dernière modification le : mercredi 17 octobre 2018 - 16:45:43

Fichier

cfm-ase15.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Mehdi Maamar, Nadjib Lazaar, Samir Loudni, Yahia Lebbah. Fault localization using itemset mining under constraints. Automated Software Engineering, Springer Verlag, 2017, 24 (2), pp.341-368. 〈10.1007/s10515-015-0189-z〉. 〈lirmm-01276633〉

Partager

Métriques

Consultations de la notice

282

Téléchargements de fichiers

2