Non-parametric statistical fault localization

Non-parametric statistical fault localization

Fault localization is a major activity in program debugging. To automate this time-consuming task, many existing fault-localization techniques compare passed executions and failed executions, and suggest suspicious program elements, such as predicates or statements, to facilitate the identification of faults. To do that, these techniques propose statistical models and use hypothesis testing met...

Is non-parametric hypothesis testing model robust for statistical fault localization?

Fault localization is one of the most difficult activities in software debugging. Many existing statistical faultlocalization techniques estimate the fault positions of programs by comparing the program feature spectra between passed runs and failed runs. Some existing approaches develop estimation formulas based on mean values of the underlying program feature spectra and their distributions a...

Non-parametric Statistical Inference

In most statistical problems treated in the literature a datum of the problem is the information that the various distributions of the chance variables involved belong to given families of distribution functions (d.f.'s) completely specified except for one or more parameters. Non-parametric statistical inference is concerned with problems where the d.f.'s are not specified to such an extent, an...

Benchmarking Non-Parametric Statistical Tests

Although non-parametric tests have already been proposed for that purpose, statistical significance tests for non-standard measures (different from the classification error) are less often used in the literature. This paper is an attempt at empirically verifying how these tests compare with more classical tests, on various conditions. More precisely, using a very large dataset to estimate the w...

Statistical causal analysis for fault localization