Academic Journal
Optimizing Pi–Sigma Neural Networks for software defect prediction via correlation-based feature selection and robust evaluation.
| Title: | Optimizing Pi–Sigma Neural Networks for software defect prediction via correlation-based feature selection and robust evaluation. |
|---|---|
| Authors: | Ndahi, Barka Piyinkir, Abisoye, Opeyemi Aderiike, Ojerinde, Oluwaseun Adeniyi, Aliyu, Hamzat Olanrewaju |
| Source: | Bulletin of the National Research Centre; 7/27/2026, Vol. 50 Issue 1, p1-8, 8p |
| Subject Terms: | Feature selection, Artificial neural networks, Defect tracking (Computer software development), Evaluation methodology, Model validation |
| Abstract: | Software defect prediction is essential for maintaining code quality in critical domains, yet it remains challenging due to feature redundancy and class imbalance. This study proposes an optimized Pi–Sigma Neural Network (PSNN) framework leveraging Correlation-Based Feature Selection (CBFS) and Min-Max normalization. Utilizing the NASA PROMISE CM1 dataset, a 5-fold stratified cross-validation pipeline was implemented to ensure statistical robustness and prevent data leakage. Experimental results on the CM1 dataset show the refined PSNN achieves high performance (99.80% accuracy on CM1) after aggressive feature reduction to 3–5 features and a precision of 1.000. To address class imbalance, the model achieved a Matthews Correlation Coefficient (MCC) of 0.988 and a G-Mean of 0.990. Comparative analysis shows that the developed PSNN-FS, despite its simplicity, achieves strong performance competitive with more complex architectures on the CM1 dataset. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimizing Pi–Sigma Neural Networks for software defect prediction via correlation-based feature selection and robust evaluation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ndahi%2C+Barka+Piyinkir%22">Ndahi, Barka Piyinkir</searchLink><br /><searchLink fieldCode="AR" term="%22Abisoye%2C+Opeyemi+Aderiike%22">Abisoye, Opeyemi Aderiike</searchLink><br /><searchLink fieldCode="AR" term="%22Ojerinde%2C+Oluwaseun+Adeniyi%22">Ojerinde, Oluwaseun Adeniyi</searchLink><br /><searchLink fieldCode="AR" term="%22Aliyu%2C+Hamzat+Olanrewaju%22">Aliyu, Hamzat Olanrewaju</searchLink> – Name: TitleSource Label: Source Group: Src Data: Bulletin of the National Research Centre; 7/27/2026, Vol. 50 Issue 1, p1-8, 8p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Defect+tracking+%28Computer+software+development%29%22">Defect tracking (Computer software development)</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+methodology%22">Evaluation methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Software defect prediction is essential for maintaining code quality in critical domains, yet it remains challenging due to feature redundancy and class imbalance. This study proposes an optimized Pi–Sigma Neural Network (PSNN) framework leveraging Correlation-Based Feature Selection (CBFS) and Min-Max normalization. Utilizing the NASA PROMISE CM1 dataset, a 5-fold stratified cross-validation pipeline was implemented to ensure statistical robustness and prevent data leakage. Experimental results on the CM1 dataset show the refined PSNN achieves high performance (99.80% accuracy on CM1) after aggressive feature reduction to 3–5 features and a precision of 1.000. To address class imbalance, the model achieved a Matthews Correlation Coefficient (MCC) of 0.988 and a G-Mean of 0.990. Comparative analysis shows that the developed PSNN-FS, despite its simplicity, achieves strong performance competitive with more complex architectures on the CM1 dataset. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Bulletin of the National Research Centre 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: 10.1186/s42269-026-01453-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 1 Subjects: – SubjectFull: Feature selection Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Defect tracking (Computer software development) Type: general – SubjectFull: Evaluation methodology Type: general – SubjectFull: Model validation Type: general Titles: – TitleFull: Optimizing Pi–Sigma Neural Networks for software defect prediction via correlation-based feature selection and robust evaluation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ndahi, Barka Piyinkir – PersonEntity: Name: NameFull: Abisoye, Opeyemi Aderiike – PersonEntity: Name: NameFull: Ojerinde, Oluwaseun Adeniyi – PersonEntity: Name: NameFull: Aliyu, Hamzat Olanrewaju IsPartOfRelationships: – BibEntity: Dates: – D: 27 M: 07 Text: 7/27/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 25228307 Numbering: – Type: volume Value: 50 – Type: issue Value: 1 Titles: – TitleFull: Bulletin of the National Research Centre Type: main |
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