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FPA-FL: Incorporating static fault-proneness analysis into statistical fault localization.

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Title: FPA-FL: Incorporating static fault-proneness analysis into statistical fault localization.
Authors: Feyzi, Farid1 farid_feyzi@comp.iust.ac.ir, Parsa, Saeed1 Parsa@iust.ac.ir
Source: Journal of Systems & Software. Feb2018, Vol. 136, p39-58. 20p.
Subjects: Computer debugging software, Computer software testing, Scalability
Abstract: Despite the proven applicability of the statistical methods in automatic fault localization, these approaches are biased by data collected from different executions of the program. This biasness could result in unstable statistical models which may vary dependent on test data provided for trial executions of the program. To resolve the difficulty, in this article a new ‘fault-proneness’-aware statistical approach based on Elastic-Net regression, namely FPA-FL is proposed. The main idea behind FPA-FL is to consider the static structure and the fault-proneness of the program statements in addition to their dynamic correlations with the program termination state. The grouping effect of FPA-FL is helpful for finding multiple faults and supporting scalability. To provide the context of failure, cause-effect chains of program faults are discovered. FPA-FL is evaluated from different viewpoints on well-known test suites. The results reveal high fault localization performance of our approach, compared with similar techniques in the literature. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Systems & Software is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 126515163
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Systems+%26+Software%22">Journal of Systems & Software</searchLink>. Feb2018, Vol. 136, p39-58. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Computer+debugging+software%22">Computer debugging software</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+testing%22">Computer software testing</searchLink><br /><searchLink fieldCode="DE" term="%22Scalability%22">Scalability</searchLink>
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  Data: Despite the proven applicability of the statistical methods in automatic fault localization, these approaches are biased by data collected from different executions of the program. This biasness could result in unstable statistical models which may vary dependent on test data provided for trial executions of the program. To resolve the difficulty, in this article a new ‘fault-proneness’-aware statistical approach based on Elastic-Net regression, namely FPA-FL is proposed. The main idea behind FPA-FL is to consider the static structure and the fault-proneness of the program statements in addition to their dynamic correlations with the program termination state. The grouping effect of FPA-FL is helpful for finding multiple faults and supporting scalability. To provide the context of failure, cause-effect chains of program faults are discovered. FPA-FL is evaluated from different viewpoints on well-known test suites. The results reveal high fault localization performance of our approach, compared with similar techniques in the literature. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Journal of Systems & Software is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.jss.2017.11.002
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      – Code: eng
        Text: English
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        PageCount: 20
        StartPage: 39
    Subjects:
      – SubjectFull: Computer debugging software
        Type: general
      – SubjectFull: Computer software testing
        Type: general
      – SubjectFull: Scalability
        Type: general
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      – TitleFull: FPA-FL: Incorporating static fault-proneness analysis into statistical fault localization.
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            NameFull: Feyzi, Farid
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            NameFull: Parsa, Saeed
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              Text: Feb2018
              Type: published
              Y: 2018
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              Value: 136
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            – TitleFull: Journal of Systems & Software
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