Academic Journal
An explainable AI framework for enhanced software defect prediction using transformer-assisted boosting.
| Τίτλος: | An explainable AI framework for enhanced software defect prediction using transformer-assisted boosting. |
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| Συγγραφείς: | Kun, Qi, Shaikh, Zaffar Ahmed, Yang, Jing, Kumar, Gyanendra, Mohammed, Heba Abdelgader, Yee, Por Lip |
| Πηγή: | Scientific Reports; 6/4/2026, Vol. 16 Issue 1, p1-17, 17p |
| Θεματικοί όροι: | Transformer models, Boosting algorithms, Shapley Additive Explanations, Artificial intelligence, Defect tracking (Computer software development), Software measurement, Machine learning |
| Περίληψη: | Accurate defect prediction is essential for better software quality to avoid cost overruns, schedule delays, and reduced system reliability due to software defects. This study presents a Transformer Assisted Boosting Framework (TABF) that combines XGBoost with the Transformer's self-attention to achieve higher predictive accuracy and interpretability. The framework is evaluated using the NASA Metrics Data Program (MDP) and the Code4Code dataset, which comprises software metrics such as cyclomatic complexity, Halstead's properties, and lines of code. Experimental results demonstrate that the performance of TABF, with AUC scores of 0.95 and ROC of 0.96, is superior to classical machine learning models, such as Random Forest and SVM, with accuracies of 92.5% and 94.3%, respectively. SHapley Additive exPlanations (SHAP) are used to explain feature importance, uncovering that lines of code and McCabe's cyclomatic complexity are among the most important predictors of software defects. These insights are used for defect management and resource allocation, as well as to improve software reliability. TABF unifies high-performance predictive modeling with explainability and closes the gap between machine learning or deep learning-based defect prediction models and their use by software quality assurance practitioners in practice. [ABSTRACT FROM AUTHOR] |
| Copyright of Scientific Reports is the property of Springer Nature 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1038/s41598-026-44202-3 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Header | DbId: edb DbLabel: Complementary Index An: 194256174 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.4189453125 |
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| Items | – Name: Title Label: Title Group: Ti Data: An explainable AI framework for enhanced software defect prediction using transformer-assisted boosting. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kun%2C+Qi%22">Kun, Qi</searchLink><br /><searchLink fieldCode="AR" term="%22Shaikh%2C+Zaffar+Ahmed%22">Shaikh, Zaffar Ahmed</searchLink><br /><searchLink fieldCode="AR" term="%22Yang%2C+Jing%22">Yang, Jing</searchLink><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Gyanendra%22">Kumar, Gyanendra</searchLink><br /><searchLink fieldCode="AR" term="%22Mohammed%2C+Heba+Abdelgader%22">Mohammed, Heba Abdelgader</searchLink><br /><searchLink fieldCode="AR" term="%22Yee%2C+Por+Lip%22">Yee, Por Lip</searchLink> – Name: TitleSource Label: Source Group: Src Data: Scientific Reports; 6/4/2026, Vol. 16 Issue 1, p1-17, 17p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Shapley+Additive+Explanations%22">Shapley Additive Explanations</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Defect+tracking+%28Computer+software+development%29%22">Defect tracking (Computer software development)</searchLink><br /><searchLink fieldCode="DE" term="%22Software+measurement%22">Software measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate defect prediction is essential for better software quality to avoid cost overruns, schedule delays, and reduced system reliability due to software defects. This study presents a Transformer Assisted Boosting Framework (TABF) that combines XGBoost with the Transformer's self-attention to achieve higher predictive accuracy and interpretability. The framework is evaluated using the NASA Metrics Data Program (MDP) and the Code4Code dataset, which comprises software metrics such as cyclomatic complexity, Halstead's properties, and lines of code. Experimental results demonstrate that the performance of TABF, with AUC scores of 0.95 and ROC of 0.96, is superior to classical machine learning models, such as Random Forest and SVM, with accuracies of 92.5% and 94.3%, respectively. SHapley Additive exPlanations (SHAP) are used to explain feature importance, uncovering that lines of code and McCabe's cyclomatic complexity are among the most important predictors of software defects. These insights are used for defect management and resource allocation, as well as to improve software reliability. TABF unifies high-performance predictive modeling with explainability and closes the gap between machine learning or deep learning-based defect prediction models and their use by software quality assurance practitioners in practice. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Scientific Reports is the property of Springer Nature 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: 10.1038/s41598-026-44202-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Transformer models Type: general – SubjectFull: Boosting algorithms Type: general – SubjectFull: Shapley Additive Explanations Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Defect tracking (Computer software development) Type: general – SubjectFull: Software measurement Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: An explainable AI framework for enhanced software defect prediction using transformer-assisted boosting. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kun, Qi – PersonEntity: Name: NameFull: Shaikh, Zaffar Ahmed – PersonEntity: Name: NameFull: Yang, Jing – PersonEntity: Name: NameFull: Kumar, Gyanendra – PersonEntity: Name: NameFull: Mohammed, Heba Abdelgader – PersonEntity: Name: NameFull: Yee, Por Lip IsPartOfRelationships: – BibEntity: Dates: – D: 04 M: 06 Text: 6/4/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20452322 Numbering: – Type: volume Value: 16 – Type: issue Value: 1 Titles: – TitleFull: Scientific Reports Type: main |
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