A dynamic fault localization technique with noise reduction for java programs

Jian Xu, W. K. Chan*, Zhenyu Zhang, T. H. Tse, Shanping Li

*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

18 Citations (Scopus)

Abstract

Existing fault localization techniques combine various program features and similarity coefficients with the aim of precisely assessing the similarities among the dynamic spectra of these program features to predict the locations of faults. Many such techniques estimate the probability of a particular program feature causing the observed failures. They ignore the noise introduced by the other features on the same set of executions that may lead to the observed failures. In this paper, we propose both the use of chains of key basic blocks as program features and an innovative similarity coefficient that has noise reduction effect. We have implemented our proposal in a technique known as MKBC. We have empirically evaluated MKBC using three real-life medium-sized programs with real faults. The results show that MKBC outperforms Tarantula, Jaccard, SBI, and Ochiai significantly. © 2011 IEEE.
Original languageEnglish
Title of host publicationProceedings - International Conference on Quality Software
Pages11-20
DOIs
Publication statusPublished - 2011
Event11th International Conference on Quality Software, QSIC 2011 - Madrid, Spain
Duration: 13 Jul 201114 Jul 2011

Publication series

Name
ISSN (Print)1550-6002

Conference

Conference11th International Conference on Quality Software, QSIC 2011
PlaceSpain
CityMadrid
Period13/07/1114/07/11

Research Keywords

  • fault localization
  • key block chain
  • noise reduction

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