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Applying Cost-Sensitive Learning to Mitigate the Effect of Class Imbalance for Deep Learning-Based Fault Localization.

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Τίτλος: Applying Cost-Sensitive Learning to Mitigate the Effect of Class Imbalance for Deep Learning-Based Fault Localization.
Συγγραφείς: Yan, Yue1,2 (AUTHOR) yanyue@sdust.edu.cn, Jiang, Shujuan3 (AUTHOR) shjjiang@cumt.edu.cn, Zhang, Yanmei3 (AUTHOR), Zhang, Cheng4 (AUTHOR), Sun, Yiru1 (AUTHOR)
Πηγή: International Journal of Software Engineering & Knowledge Engineering. Sep2026, p1-34. 34p. 2 Illustrations.
Θεματικοί όροι: Computer software testing, Fault diagnosis, Machine learning, Deep learning
Περίληψη: Automated software fault localization has become a research focus with the growing software scale. Deep learning-based fault localization (DLFL) has been widely studied due to its excellent performance. Most existing DLFL methods use the execution traces of the test cases as features. However, the number of passing test cases is far more than that of failed test cases in the practical test suite. The severe class imbalance problem is ignored in existing DLFL methods. To alleviate the impact of the class imbalance on DLFL, we propose applying cost-sensitive learning to set different weights for each sample to optimize the performance of fault localization. We reduce the number of passing test cases and compute the weight for each sample. The sample weight is combined with a loss function to optimize the accuracy of the DLFL method. To evaluate our approach, we conduct within-project and cross-project fault localization experiments on the Defects4J benchmark. Experimental results show our approach outperforms the traditional DLFL and existing data augmentation methods in fault localization (e.g. localizing 133 faults within Top-5). The experimental results show that applying cost-sensitive learning can effectively alleviate the class imbalance problem in DLFL. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Software Engineering & Knowledge Engineering is the property of World Scientific Publishing Company 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.)
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IllustrationInfo
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  Data: Applying Cost-Sensitive Learning to Mitigate the Effect of Class Imbalance for Deep Learning-Based Fault Localization.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Software+Engineering+%26+Knowledge+Engineering%22">International Journal of Software Engineering & Knowledge Engineering</searchLink>. Sep2026, p1-34. 34p. 2 Illustrations.
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  Data: <searchLink fieldCode="DE" term="%22Computer+software+testing%22">Computer software testing</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
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  Data: Automated software fault localization has become a research focus with the growing software scale. Deep learning-based fault localization (DLFL) has been widely studied due to its excellent performance. Most existing DLFL methods use the execution traces of the test cases as features. However, the number of passing test cases is far more than that of failed test cases in the practical test suite. The severe class imbalance problem is ignored in existing DLFL methods. To alleviate the impact of the class imbalance on DLFL, we propose applying cost-sensitive learning to set different weights for each sample to optimize the performance of fault localization. We reduce the number of passing test cases and compute the weight for each sample. The sample weight is combined with a loss function to optimize the accuracy of the DLFL method. To evaluate our approach, we conduct within-project and cross-project fault localization experiments on the Defects4J benchmark. Experimental results show our approach outperforms the traditional DLFL and existing data augmentation methods in fault localization (e.g. localizing 133 faults within Top-5). The experimental results show that applying cost-sensitive learning can effectively alleviate the class imbalance problem in DLFL. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Software Engineering & Knowledge Engineering is the property of World Scientific Publishing Company 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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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1142/s0218194026500737
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 34
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      – SubjectFull: Computer software testing
        Type: general
      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Deep learning
        Type: general
    Titles:
      – TitleFull: Applying Cost-Sensitive Learning to Mitigate the Effect of Class Imbalance for Deep Learning-Based Fault Localization.
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            – D: 26
              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
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