Adaptive Hybrid Learning Framework for Software Fault Prediction in Smart Manufacturing Systems Using Class Imbalance Optimization.
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| Title: | Adaptive Hybrid Learning Framework for Software Fault Prediction in Smart Manufacturing Systems Using Class Imbalance Optimization. |
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| Authors: | Singh, Baljeet1 baljeet.16938@lpu.co.in |
| Source: | International Journal of Performability Engineering. Jun2026, Vol. 22 Issue 6, p331-340. 10p. |
| Subjects: | Software reliability, Feature extraction, Machine learning, Manufacturing process automation, Ensemble learning, Automation |
| Abstract: | Smart manufacturing systems based on software become very dependent on embedded control applications, industrial automation software and real-time decision mechanisms to ensure the reliability of production. Nonetheless, software failures that may arise in such systems are usually not foreseeable because datasets are highly unbalanced, in which the number of faulty software modules is much less than the number of non-faulty modules. This paper will present an adaptive hybrid learning paradigm of software fault prediction in smart manufacturing systems through class imbalance optimization. The proposed framework is composed of three significant modules: (1) Feature Extraction Module that is used to extract software metrics, execution logs and operational indicators; (2) Class Imbalance Optimization Module that applies Synthetic Minority Oversampling Technique (SMOTE) and Borderline-SMOTE to equalize minority fault classes; and (3) Hybrid Prediction Module that implements the use of the random forest, support vector machine, Multi-Layer Perceptron and Bayesian Network through weighted voting classification. NASA software defect repositories and PROMISE Repository datasets are benchmark software defect datasets on which models are validated. As shown by the results of the experiment, the proposed framework has a prediction accuracy of 97.1%, precision of 96.2 and a recall of 95.8, which is superior to traditional single classifiers. The framework enhances the detection of minority faults, minimizes false negatives and facilitates predictor software maintenance in industrial automation conditions. Future directions of work are explainable fault prediction and real-time deployment in edge-based manufacturing systems. [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: 194978493 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Adaptive Hybrid Learning Framework for Software Fault Prediction in Smart Manufacturing Systems Using Class Imbalance Optimization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Singh%2C+Baljeet%22">Singh, Baljeet</searchLink><relatesTo>1</relatesTo><i> baljeet.16938@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>. Jun2026, Vol. 22 Issue 6, p331-340. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Software+reliability%22">Software reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+process+automation%22">Manufacturing process automation</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Smart manufacturing systems based on software become very dependent on embedded control applications, industrial automation software and real-time decision mechanisms to ensure the reliability of production. Nonetheless, software failures that may arise in such systems are usually not foreseeable because datasets are highly unbalanced, in which the number of faulty software modules is much less than the number of non-faulty modules. This paper will present an adaptive hybrid learning paradigm of software fault prediction in smart manufacturing systems through class imbalance optimization. The proposed framework is composed of three significant modules: (1) Feature Extraction Module that is used to extract software metrics, execution logs and operational indicators; (2) Class Imbalance Optimization Module that applies Synthetic Minority Oversampling Technique (SMOTE) and Borderline-SMOTE to equalize minority fault classes; and (3) Hybrid Prediction Module that implements the use of the random forest, support vector machine, Multi-Layer Perceptron and Bayesian Network through weighted voting classification. NASA software defect repositories and PROMISE Repository datasets are benchmark software defect datasets on which models are validated. As shown by the results of the experiment, the proposed framework has a prediction accuracy of 97.1%, precision of 96.2 and a recall of 95.8, which is superior to traditional single classifiers. The framework enhances the detection of minority faults, minimizes false negatives and facilitates predictor software maintenance in industrial automation conditions. Future directions of work are explainable fault prediction and real-time deployment in edge-based manufacturing systems. [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.26.06.p4.331340 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 331 Subjects: – SubjectFull: Software reliability Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Manufacturing process automation Type: general – SubjectFull: Ensemble learning Type: general – SubjectFull: Automation Type: general Titles: – TitleFull: Adaptive Hybrid Learning Framework for Software Fault Prediction in Smart Manufacturing Systems Using Class Imbalance Optimization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Singh, Baljeet IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09731318 Numbering: – Type: volume Value: 22 – Type: issue Value: 6 Titles: – TitleFull: International Journal of Performability Engineering Type: main |
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