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Article: A general noise-reduction framework for fault localization of Java programs
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TitleA general noise-reduction framework for fault localization of Java programs
 
AuthorsXu, J3
Zhang, Z1
Chan, WK4
Tse, TH2
Li, S3
 
KeywordsFault localization
Key block chain
Noise reduction
Program debugging
 
Issue Date2013
 
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/infsof
 
CitationInformation and Software Technology, 2013, v. 55 n. 5, p. 880–896 [How to Cite?]
DOI: http://dx.doi.org/10.1016/j.infsof.2012.08.006
 
AbstractContext: 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 often ignore the noise introduced by other features on the same set of executions that may lead to the observed failures. It is unclear to what extent such noise can be alleviated. Objective: This paper aims to develop a framework that reduces the noise in fault-failure correlation measurements. Method: We develop a fault-localization framework that uses chains of key basic blocks as program features and a noise-reduction methodology to improve on the similarity coefficients of fault-localization techniques. We evaluate our framework on five base techniques using five real-life median-scaled programs in different application domains. We also conduct a case study on subjects with multiple faults. Results: The experimental result shows that the synthesized techniques are more effective than their base techniques by almost 10%. Moreover, their runtime overhead factors to collect the required feature values are practical. The case study also shows that the synthesized techniques work well on subjects with multiple faults. Conclusion: We conclude that the proposed framework has a significant and positive effect on improving the effectiveness of the corresponding base techniques. © 2012 Elsevier B.V. All rights reserved.
 
ISSN0950-5849
2013 Impact Factor: 1.328
2013 SCImago Journal Rankings: 1.072
 
DOIhttp://dx.doi.org/10.1016/j.infsof.2012.08.006
 
DC FieldValue
dc.contributor.authorXu, J
 
dc.contributor.authorZhang, Z
 
dc.contributor.authorChan, WK
 
dc.contributor.authorTse, TH
 
dc.contributor.authorLi, S
 
dc.date.accessioned2012-09-20T08:24:24Z
 
dc.date.available2012-09-20T08:24:24Z
 
dc.date.issued2013
 
dc.description.abstractContext: 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 often ignore the noise introduced by other features on the same set of executions that may lead to the observed failures. It is unclear to what extent such noise can be alleviated. Objective: This paper aims to develop a framework that reduces the noise in fault-failure correlation measurements. Method: We develop a fault-localization framework that uses chains of key basic blocks as program features and a noise-reduction methodology to improve on the similarity coefficients of fault-localization techniques. We evaluate our framework on five base techniques using five real-life median-scaled programs in different application domains. We also conduct a case study on subjects with multiple faults. Results: The experimental result shows that the synthesized techniques are more effective than their base techniques by almost 10%. Moreover, their runtime overhead factors to collect the required feature values are practical. The case study also shows that the synthesized techniques work well on subjects with multiple faults. Conclusion: We conclude that the proposed framework has a significant and positive effect on improving the effectiveness of the corresponding base techniques. © 2012 Elsevier B.V. All rights reserved.
 
dc.description.naturepostprint
 
dc.identifier.citationInformation and Software Technology, 2013, v. 55 n. 5, p. 880–896 [How to Cite?]
DOI: http://dx.doi.org/10.1016/j.infsof.2012.08.006
 
dc.identifier.citeulike11262191
 
dc.identifier.doihttp://dx.doi.org/10.1016/j.infsof.2012.08.006
 
dc.identifier.hkuros207622
 
dc.identifier.hkuros214180
 
dc.identifier.issn0950-5849
2013 Impact Factor: 1.328
2013 SCImago Journal Rankings: 1.072
 
dc.identifier.scopuseid_2-s2.0-84875228971
 
dc.identifier.urihttp://hdl.handle.net/10722/165835
 
dc.languageeng
 
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/infsof
 
dc.publisher.placeNetherlands
 
dc.relation.ispartofInformation and Software Technology
 
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Information and Software Technology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information and Software Technology, 2012. DOI: 10.1016/j.infsof.2012.08.006
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.subjectFault localization
 
dc.subjectKey block chain
 
dc.subjectNoise reduction
 
dc.subjectProgram debugging
 
dc.titleA general noise-reduction framework for fault localization of Java programs
 
dc.typeArticle
 
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<item><contributor.author>Xu, J</contributor.author>
<contributor.author>Zhang, Z</contributor.author>
<contributor.author>Chan, WK</contributor.author>
<contributor.author>Tse, TH</contributor.author>
<contributor.author>Li, S</contributor.author>
<date.accessioned>2012-09-20T08:24:24Z</date.accessioned>
<date.available>2012-09-20T08:24:24Z</date.available>
<date.issued>2013</date.issued>
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<identifier.uri>http://hdl.handle.net/10722/165835</identifier.uri>
<description.abstract>Context: 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 often ignore the noise introduced by other features on the same set of executions that may lead to the observed failures. It is unclear to what extent such noise can be alleviated. Objective: This paper aims to develop a framework that reduces the noise in fault-failure correlation measurements. Method: We develop a fault-localization framework that uses chains of key basic blocks as program features and a noise-reduction methodology to improve on the similarity coefficients of fault-localization techniques. We evaluate our framework on five base techniques using five real-life median-scaled programs in different application domains. We also conduct a case study on subjects with multiple faults. Results: The experimental result shows that the synthesized techniques are more effective than their base techniques by almost 10%. Moreover, their runtime overhead factors to collect the required feature values are practical. The case study also shows that the synthesized techniques work well on subjects with multiple faults. Conclusion: We conclude that the proposed framework has a significant and positive effect on improving the effectiveness of the corresponding base techniques. &#169; 2012 Elsevier B.V. All rights reserved.</description.abstract>
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<subject>Fault localization</subject>
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Author Affiliations
  1. Institute of Software Chinese Academy of Sciences
  2. The University of Hong Kong
  3. Zhejiang University
  4. City University of Hong Kong