Adaptive Ensemble Learning for Software Defect Prediction with Imbalanced Data.
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| Title: | Adaptive Ensemble Learning for Software Defect Prediction with Imbalanced Data. |
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| Authors: | Mehta, Ashu1 ashu.23631@lpu.co.in |
| Source: | International Journal of Performability Engineering. Mar2026, Vol. 22 Issue 3, p167-177. 11p. |
| Subjects: | Ensemble learning, Adaptive sampling (Statistics), Machine learning, Defect tracking (Computer software development) |
| Abstract: | Software Fault Prediction (SFP) plays a very crucial role in improving software reliability by facilitating the early detection of modules prone to defects. Nevertheless, ongoing issues like extreme imbalance in classes and unstable performance of the classifiers on the heterogeneous datasets deter the efficiency of current methods. To address these problems, in this paper, a stability-conscious meta-ensemble learning architecture is proposed combining adaptive sampling with meta-level classifier fusion. Contrary to traditional ensemble-based approaches that rely on resampling and fixed combinations of models, the presented architecture dynamically chooses the appropriate sampling techniques to rely on the properties of the data and trains the best combination of classifiers with the help of a meta-learner. Wide experiments performed on benchmark datasets of PROMISE, NASA, AEEEM, ReLink, and SoftLab indicate that there is a consistent improvement in performance compared to baseline ensemble models with better AUC, MCC, and G-mean. Moreover, the experiments of the cross-project fault prediction prove high generalization and low deterioration of performance. The statistical significance tests such as Wilcoxon Signed-Rank Test, Cliff- Delta, and Nemenyi post-hoc tests confirm the strength of the suggested method. In general, the framework offers a practical and generalizable method of resolving the issues of class imbalance and performance instability in the real-world software fault prediction. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192187928 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Adaptive Ensemble Learning for Software Defect Prediction with Imbalanced Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mehta%2C+Ashu%22">Mehta, Ashu</searchLink><relatesTo>1</relatesTo><i> ashu.23631@lpu.co.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Performability+Engineering%22">International Journal of Performability Engineering</searchLink>. Mar2026, Vol. 22 Issue 3, p167-177. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+sampling+%28Statistics%29%22">Adaptive sampling (Statistics)</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: Software Fault Prediction (SFP) plays a very crucial role in improving software reliability by facilitating the early detection of modules prone to defects. Nevertheless, ongoing issues like extreme imbalance in classes and unstable performance of the classifiers on the heterogeneous datasets deter the efficiency of current methods. To address these problems, in this paper, a stability-conscious meta-ensemble learning architecture is proposed combining adaptive sampling with meta-level classifier fusion. Contrary to traditional ensemble-based approaches that rely on resampling and fixed combinations of models, the presented architecture dynamically chooses the appropriate sampling techniques to rely on the properties of the data and trains the best combination of classifiers with the help of a meta-learner. Wide experiments performed on benchmark datasets of PROMISE, NASA, AEEEM, ReLink, and SoftLab indicate that there is a consistent improvement in performance compared to baseline ensemble models with better AUC, MCC, and G-mean. Moreover, the experiments of the cross-project fault prediction prove high generalization and low deterioration of performance. The statistical significance tests such as Wilcoxon Signed-Rank Test, Cliff- Delta, and Nemenyi post-hoc tests confirm the strength of the suggested method. In general, the framework offers a practical and generalizable method of resolving the issues of class imbalance and performance instability in the real-world software fault prediction. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. 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: 10.23940/ijpe.26.03.p6.167177 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 167 Subjects: – SubjectFull: Ensemble learning Type: general – SubjectFull: Adaptive sampling (Statistics) Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Defect tracking (Computer software development) Type: general Titles: – TitleFull: Adaptive Ensemble Learning for Software Defect Prediction with Imbalanced Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mehta, Ashu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09731318 Numbering: – Type: volume Value: 22 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Performability Engineering Type: main |
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