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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, the authors propose both the use of chains of key basic blocks as program features and an innovative similarity coefficient that has noise reduction effect. They have implemented their proposal in a technique known as MKBC. They have empirically evaluated MKBC using three real-life medium-sized programs with real faults.
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