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Improving fault localization via weighted execution graph and graph attention network.

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Title: Improving fault localization via weighted execution graph and graph attention network.
Authors: Yan, Yue1 (AUTHOR), Jiang, Shujuan1,2 (AUTHOR) shjjiang@cumt.edu.cn, Zhang, Yanmei1,2 (AUTHOR), Zhang, Cheng1 (AUTHOR)
Source: Journal of Software: Evolution & Process. Jun2024, Vol. 36 Issue 6, p1-16. 16p.
Subjects: Software localization, Tarantulas, Problem solving
Abstract: Software fault localization is commonly recognized as arduous and time consuming. Spectrum‐based fault localization (SBFL) has been widely used due to its lightness. However, the effectiveness of SBFL is limited since it only considers simple statistics on the coverage information, ignoring the tie problem that the spectrum matrixes of some statements are the same. Most existing deep learning‐based fault localization (DLFL) techniques convert the coverage information into a vector, which utilizes the spectrum in a simplified manner and still has limitations in practice. To solve the above problem, we propose an approach via the weighted execution graph and graph attention network (WEGAT). We use a graph structure to represent the coverage information between test cases and program elements. Then, we generate a weighted execution graph by applying the predicate execution sequence. Furthermore, we combine the weighted execution graph with the AST as an integrated graph, which is the input of the GAT for fault localization. We evaluate WEGAT in within‐project and cross‐project prediction scenarios on the Defects4J benchmark. Experimental results show that our approach outperforms traditional SBFL (Ochiai, DStar and Tarantula) and DLFL (TraPT, CNN‐FL, Grace, and AGFL) methods, effectively improving the accuracy of fault localization. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Software: Evolution & Process is the property of Wiley-Blackwell 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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  Data: Improving fault localization via weighted execution graph and graph attention network.
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  Data: <searchLink fieldCode="AR" term="%22Yan%2C+Yue%22">Yan, Yue</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Shujuan%22">Jiang, Shujuan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> shjjiang@cumt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yanmei%22">Zhang, Yanmei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Cheng%22">Zhang, Cheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Software%3A+Evolution+%26+Process%22">Journal of Software: Evolution & Process</searchLink>. Jun2024, Vol. 36 Issue 6, p1-16. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Software+localization%22">Software localization</searchLink><br /><searchLink fieldCode="DE" term="%22Tarantulas%22">Tarantulas</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Software fault localization is commonly recognized as arduous and time consuming. Spectrum‐based fault localization (SBFL) has been widely used due to its lightness. However, the effectiveness of SBFL is limited since it only considers simple statistics on the coverage information, ignoring the tie problem that the spectrum matrixes of some statements are the same. Most existing deep learning‐based fault localization (DLFL) techniques convert the coverage information into a vector, which utilizes the spectrum in a simplified manner and still has limitations in practice. To solve the above problem, we propose an approach via the weighted execution graph and graph attention network (WEGAT). We use a graph structure to represent the coverage information between test cases and program elements. Then, we generate a weighted execution graph by applying the predicate execution sequence. Furthermore, we combine the weighted execution graph with the AST as an integrated graph, which is the input of the GAT for fault localization. We evaluate WEGAT in within‐project and cross‐project prediction scenarios on the Defects4J benchmark. Experimental results show that our approach outperforms traditional SBFL (Ochiai, DStar and Tarantula) and DLFL (TraPT, CNN‐FL, Grace, and AGFL) methods, effectively improving the accuracy of fault localization. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Software: Evolution & Process is the property of Wiley-Blackwell 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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        Value: 10.1002/smr.2619
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      – Code: eng
        Text: English
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        PageCount: 16
        StartPage: 1
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      – SubjectFull: Software localization
        Type: general
      – SubjectFull: Tarantulas
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      – SubjectFull: Problem solving
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      – TitleFull: Improving fault localization via weighted execution graph and graph attention network.
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            NameFull: Yan, Yue
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            NameFull: Jiang, Shujuan
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            NameFull: Zhang, Yanmei
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          Dates:
            – D: 01
              M: 06
              Text: Jun2024
              Type: published
              Y: 2024
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              Value: 36
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