Mutation-Based Graph Inference for Fault Localization

Musco, Vincenzo; Monperrus, Martin; Preux, Philippe

Type de document :

Communication dans un congrès avec actes

DOI :

10.1109/SCAM.2016.24

Titre :

Mutation-Based Graph Inference for Fault Localization

Auteur(s) :

Musco, Vincenzo [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
Sequential Learning [SEQUEL]
Monperrus, Martin [Auteur]

Self-adaptation for distributed services and large software systems [SPIRALS]
Université de Lille, Sciences et Technologies
Preux, Philippe [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]

Titre de la manifestation scientifique :

International Working Conference on Source Code Analysis and Manipulation

Ville :

Raleigh

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2016-10-02

Discipline(s) HAL :

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

Résumé en anglais : [en]

We present a new fault localization algorithm, called Vautrin, built on an approximation of causality based on call graphs. The approximation of causality is done using software mutants. The key idea is that if a mutant ...
Lire la suite >We present a new fault localization algorithm, called Vautrin, built on an approximation of causality based on call graphs. The approximation of causality is done using software mutants. The key idea is that if a mutant is killed by a test, certain call graph edges within a path between the mutation point and the failing test are likely causal. We evaluate our approach on the fault localization benchmark by Steimann et al. totaling 5,836 faults. The causal graphs are extracted from 88,732 nodes connected by 119,531 edges. Vautrin improves the fault localization effectiveness for all subjects of the benchmark. Considering the wasted effort at the method level, a classical fault localization evaluation metric, the improvement ranges from 3% to 55%, with an average improvement of 14%.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Commentaire :

update for oadoi on Nov 02 2018

Collections :

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

Source :

Harvested from HAL

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