Mutation-Based Graph Inference for Fault Localization

Musco, Vincenzo; Monperrus, Martin; Preux, Philippe

Document type :

Communication dans un congrès avec actes

DOI :

10.1109/SCAM.2016.24

Title :

Mutation-Based Graph Inference for Fault Localization

Author(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]

Conference title :

International Working Conference on Source Code Analysis and Manipulation

City :

Raleigh

Country :

Etats-Unis d'Amérique

Start date of the conference :

2016-10-02

HAL domain(s) :

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

English abstract : [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 ...
Show more >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%.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Comment :

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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