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
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01276633
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Submitted on : Wednesday, October 17, 2018 - 11:21:48 AM
Last modification on : Thursday, February 7, 2019 - 5:32:17 PM
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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⟩

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