Proceedings of the 8th International Conference on Quality
Software (QSIC '08),
IEEE Computer Society Press, Los Alamitos, CA, pp. 385-395 (2008)
Peifeng Hu 2 , Zhenyu Zhang 2 , W.K. Chan 3 , and T.H. Tse 4
[paper from IEEE Xplore | paper from IEEE digital library | technical report TR-2008-08]
Fault localization is a major activity in software debugging.
Many existing statistical fault localization techniques compare
feature spectra of successful and failed runs.
Some approaches, such as SOBER, test the similarity of the feature
spectra through parametric self-proposed hypothesis testing models.
Our finding shows, however, that the assumption on feature spectra
forming known distributions is not well-supported by empirical data.
Instead, having a simple, robust, and explanatory model is an
essential move toward establishing a debugging theory.
This paper proposes a non-parametric approach to
measuring the similarity of the feature spectra of successful and
failed runs, and picks a general hypothesis testing model, namely
the Mann-Whitney test, as the core.
The empirical results on the Siemens suite show that our technique
can outperform existing predicate-based statistical fault
localization techniques in locating faulty statements.
Keywords: Fault localization, non-parametric statistics
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