Mutation-based graph inference for fault localization

Vincenzo Musco, Martin Monperrus, Philippe Preux

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

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%.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE 16th International Working Conference on Source Code Analysis and Manipulation, SCAM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages97-106
Number of pages10
ISBN (Electronic)9781509038503
DOIs
StatePublished - Dec 12 2016
Event16th IEEE International Working Conference on Source Code Analysis and Manipulation, SCAM 2016 - Raleigh, United States
Duration: Oct 2 2016Oct 3 2016

Publication series

NameProceedings - 2016 IEEE 16th International Working Conference on Source Code Analysis and Manipulation, SCAM 2016

Other

Other16th IEEE International Working Conference on Source Code Analysis and Manipulation, SCAM 2016
CountryUnited States
CityRaleigh
Period10/2/1610/3/16

All Science Journal Classification (ASJC) codes

  • Software
  • Computational Theory and Mathematics

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