A Dynamic Fault Localization Technique with Noise Reduction for Java Programs

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  mplemented  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.
Grants:GRF 111410, GRF 716507, CityU 7008039, NSFC 61003027, NSFC 61073006
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Remarks: Selected by the conference program chairs for journal extension.
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