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

TitleIs non-parametric hypothesis testing model robust for statistical fault localization?
Authors
KeywordsFault localization
Hypothesis testing
Non-parametric
Normality
Issue Date2009
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/infsof
Citation
Information And Software Technology, 2009, v. 51 n. 11, p. 1573-1585 How to Cite?
AbstractFault localization is one of the most difficult activities in software debugging. Many existing statistical fault-localization 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 alike. Our previous work advocates the use of a non-parametric approach in estimation formulas to pinpoint fault-relevant positions. It is worthy of further study to resolve the two schools of thought by examining the fundamental, underlying properties of distributions related to fault localization. In particular, we ask: Can the feature spectra of program elements be safely considered as normal distributions so that parametric techniques can be soundly and powerfully applied? In this paper, we empirically investigate this question from the program predicate perspective. We conduct an experimental study based on the Siemens suite of programs. We examine the degree of normality on the distributions of evaluation biases of the predicates, and obtain three major results from the study. First, almost all examined distributions of evaluation biases are either normal or far from normal, but not in between. Second, the most fault-relevant predicates are less likely to exhibit normal distributions in terms of evaluation biases than other predicates. Our results show that normality is not common as far as evaluation bias can represent. Furthermore, the effectiveness of our non-parametric predicate-based fault-localization technique weakly correlates with the distributions of evaluation biases, making the technique robust to this type of uncertainty in the underlying program spectra. © 2009 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/89138
ISSN
2015 Impact Factor: 1.569
2015 SCImago Journal Rankings: 0.920
ISI Accession Number ID
Funding AgencyGrant Number
Research Grants Council of Hong Kong123207
716507
City University of Hong Kong7002324
7002464
Funding Information:

This research is supported in part by GRF grants of the Research Grants Council of Hong Kong (Project Nos. 123207 and 716507) and SRG grants of City University of Hong Kong (Project Nos. 7002324 and 7002464).

References

 

DC FieldValueLanguage
dc.contributor.authorZhang, Zen_HK
dc.contributor.authorChan, WKen_HK
dc.contributor.authorTse, THen_HK
dc.contributor.authorHu, Pen_HK
dc.contributor.authorWang, Xen_HK
dc.date.accessioned2010-09-06T09:52:51Z-
dc.date.available2010-09-06T09:52:51Z-
dc.date.issued2009en_HK
dc.identifier.citationInformation And Software Technology, 2009, v. 51 n. 11, p. 1573-1585en_HK
dc.identifier.issn0950-5849en_HK
dc.identifier.urihttp://hdl.handle.net/10722/89138-
dc.description.abstractFault localization is one of the most difficult activities in software debugging. Many existing statistical fault-localization 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 alike. Our previous work advocates the use of a non-parametric approach in estimation formulas to pinpoint fault-relevant positions. It is worthy of further study to resolve the two schools of thought by examining the fundamental, underlying properties of distributions related to fault localization. In particular, we ask: Can the feature spectra of program elements be safely considered as normal distributions so that parametric techniques can be soundly and powerfully applied? In this paper, we empirically investigate this question from the program predicate perspective. We conduct an experimental study based on the Siemens suite of programs. We examine the degree of normality on the distributions of evaluation biases of the predicates, and obtain three major results from the study. First, almost all examined distributions of evaluation biases are either normal or far from normal, but not in between. Second, the most fault-relevant predicates are less likely to exhibit normal distributions in terms of evaluation biases than other predicates. Our results show that normality is not common as far as evaluation bias can represent. Furthermore, the effectiveness of our non-parametric predicate-based fault-localization technique weakly correlates with the distributions of evaluation biases, making the technique robust to this type of uncertainty in the underlying program spectra. © 2009 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/infsofen_HK
dc.relation.ispartofInformation and Software Technologyen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectFault localizationen_HK
dc.subjectHypothesis testingen_HK
dc.subjectNon-parametricen_HK
dc.subjectNormalityen_HK
dc.titleIs non-parametric hypothesis testing model robust for statistical fault localization?en_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0950-5849&volume=51&issue=11&spage=1573&epage=1585&date=2009&atitle=Is+non-parametric+hypothesis+testing+model+robust+for+statistical+fault+localization?-
dc.identifier.emailTse, TH: thtse@cs.hku.hken_HK
dc.identifier.authorityTse, TH=rp00546en_HK
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.infsof.2009.06.013en_HK
dc.identifier.scopuseid_2-s2.0-69749099373en_HK
dc.identifier.hkuros167680en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-69749099373&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume51en_HK
dc.identifier.issue11en_HK
dc.identifier.spage1573en_HK
dc.identifier.epage1585en_HK
dc.identifier.isiWOS:000270619300009-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridZhang, Z=10639502200en_HK
dc.identifier.scopusauthoridChan, WK=23967779900en_HK
dc.identifier.scopusauthoridTse, TH=7005496974en_HK
dc.identifier.scopusauthoridHu, P=7201989692en_HK
dc.identifier.scopusauthoridWang, X=34769103600en_HK
dc.identifier.citeulike5351560-

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