Academic Journal
A Unified Deep Learning Based Feature Representation Approach for Effective Software Defect Prediction.
| Title: | A Unified Deep Learning Based Feature Representation Approach for Effective Software Defect Prediction. |
|---|---|
| Authors: | Malhotra, Ruchika, Singh, Priya |
| Source: | Software: Practice & Experience; Aug2026, Vol. 56 Issue 8, p999-1019, 21p |
| Subject Terms: | Deep learning, Feature extraction, Data analytics, Software measurement, Programming language semantics, Machine learning, Defect tracking (Computer software development) |
| Abstract: | Purpose: Accurate software defect prediction (SDP) is critical to the success of any software project. Earlier studies have largely used static, semantic or structural features either in isolation or in pairs, offering a partial view of the source code. In reality, static features depict the statistical characteristics, semantic features depict the context and structural features depict data and control dependencies of the code. We propose a strong SDP model that integrates three types of features, achieving a holistic view of the source code, ultimately thereby enabling more robust and generalizable predictions. Methods: First, the model extracts the static features from the open source PROMISE repository, semantic features from the Abstract Syntax Tree via CodeBERT followed by BiGRU and structural features from the Program Dependency Graph via Graph Convolutional Network. Second, feature alignment is performed for the fixed‐set representation of the three types of features using global attention pooling. Third, feature fusion is done for joint feature representation, followed by the application of additive attention to select the most suitable features. To handle the class imbalance scenario, cost‐sensitive gradient boosting is applied to penalize misclassifications more heavily. At last, the final feature set is fed to a classifier for defect prediction. Results: Experiments conducted on eleven open source datasets reveal that the proposed unified feature representation approach achieves substantial performance improvements over the state‐of‐the‐art models. Moreover, the Wilcoxon signed‐rank test offers statistical validation for the relevance of these enhancements. Conclusion: The integration of static, semantic and structural information results in a more holistic representation of source code, which substantially enhances defect prediction performance. The proposed approach addresses the partial view constraints of earlier approaches and offers strong potential for establishing a more reliable and robust SDP across various application domains. [ABSTRACT FROM AUTHOR] |
| Copyright of Software: Practice & Experience 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: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: A Unified Deep Learning Based Feature Representation Approach for Effective Software Defect Prediction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Malhotra%2C+Ruchika%22">Malhotra, Ruchika</searchLink><br /><searchLink fieldCode="AR" term="%22Singh%2C+Priya%22">Singh, Priya</searchLink> – Name: TitleSource Label: Source Group: Src Data: Software: Practice & Experience; Aug2026, Vol. 56 Issue 8, p999-1019, 21p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Software+measurement%22">Software measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Programming+language+semantics%22">Programming language semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Defect+tracking+%28Computer+software+development%29%22">Defect tracking (Computer software development)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: Accurate software defect prediction (SDP) is critical to the success of any software project. Earlier studies have largely used static, semantic or structural features either in isolation or in pairs, offering a partial view of the source code. In reality, static features depict the statistical characteristics, semantic features depict the context and structural features depict data and control dependencies of the code. We propose a strong SDP model that integrates three types of features, achieving a holistic view of the source code, ultimately thereby enabling more robust and generalizable predictions. Methods: First, the model extracts the static features from the open source PROMISE repository, semantic features from the Abstract Syntax Tree via CodeBERT followed by BiGRU and structural features from the Program Dependency Graph via Graph Convolutional Network. Second, feature alignment is performed for the fixed‐set representation of the three types of features using global attention pooling. Third, feature fusion is done for joint feature representation, followed by the application of additive attention to select the most suitable features. To handle the class imbalance scenario, cost‐sensitive gradient boosting is applied to penalize misclassifications more heavily. At last, the final feature set is fed to a classifier for defect prediction. Results: Experiments conducted on eleven open source datasets reveal that the proposed unified feature representation approach achieves substantial performance improvements over the state‐of‐the‐art models. Moreover, the Wilcoxon signed‐rank test offers statistical validation for the relevance of these enhancements. Conclusion: The integration of static, semantic and structural information results in a more holistic representation of source code, which substantially enhances defect prediction performance. The proposed approach addresses the partial view constraints of earlier approaches and offers strong potential for establishing a more reliable and robust SDP across various application domains. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Software: Practice & Experience 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/spe.70081 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 999 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Data analytics Type: general – SubjectFull: Software measurement Type: general – SubjectFull: Programming language semantics Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Defect tracking (Computer software development) Type: general Titles: – TitleFull: A Unified Deep Learning Based Feature Representation Approach for Effective Software Defect Prediction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Malhotra, Ruchika – PersonEntity: Name: NameFull: Singh, Priya IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00380644 Numbering: – Type: volume Value: 56 – Type: issue Value: 8 Titles: – TitleFull: Software: Practice & Experience Type: main |
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