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Article: A general noise-reduction framework for fault localization of Java programs

TitleA general noise-reduction framework for fault localization of Java programs
Authors
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
Citation
Information and Software Technology, 2013, v. 55 n. 5, p. 880–896 How to Cite?
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. © 2012 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/165835
ISSN
2013 Impact Factor: 1.328
2013 SCImago Journal Rankings: 1.072
ISI Accession Number ID

 

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
DC FieldValueLanguage
dc.contributor.authorXu, Jen_HK
dc.contributor.authorZhang, Zen_HK
dc.contributor.authorChan, WKen_HK
dc.contributor.authorTse, THen_HK
dc.contributor.authorLi, Sen_HK
dc.date.accessioned2012-09-20T08:24:24Z-
dc.date.available2012-09-20T08:24:24Z-
dc.date.issued2013en_HK
dc.identifier.citationInformation and Software Technology, 2013, v. 55 n. 5, p. 880–896en_HK
dc.identifier.issn0950-5849en_HK
dc.identifier.urihttp://hdl.handle.net/10722/165835-
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.en_HK
dc.languageengen_US
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.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 localizationen_HK
dc.subjectKey block chainen_HK
dc.subjectNoise reductionen_HK
dc.subjectProgram debuggingen_HK
dc.titleA general noise-reduction framework for fault localization of Java programsen_HK
dc.typeArticleen_HK
dc.identifier.emailXu, J: jxu@zju.edu.cnen_HK
dc.identifier.emailZhang, Z: zhangzy@ios.ac.cn-
dc.identifier.emailChan, WK: wkchan@cityu.edu.hk-
dc.identifier.emailTse, TH: thtse@cs.hku.hk-
dc.identifier.emailLi, S: shan@zju.edu.cn-
dc.identifier.authorityTse, TH=rp00546en_HK
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.infsof.2012.08.006en_HK
dc.identifier.scopuseid_2-s2.0-84875228971en_HK
dc.identifier.hkuros207622en_US
dc.identifier.hkuros214180-
dc.identifier.isiWOS:000317327000007-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridLi, S=23489737100en_HK
dc.identifier.scopusauthoridTse, TH=7005496974en_HK
dc.identifier.scopusauthoridChan, WK=23967779900en_HK
dc.identifier.scopusauthoridZhang, Z=10639502200en_HK
dc.identifier.scopusauthoridXu, J=9532629300en_HK
dc.identifier.citeulike11262191-

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