Conference
Integrated frameworks for intelligent detection of software faults.
| Τίτλος: | Integrated frameworks for intelligent detection of software faults. |
|---|---|
| Συγγραφείς: | Sharma, Tarunim, Bhaskar, Shalini, Jatain, Aman, Pabreja, Kavita |
| Πηγή: | AIP Conference Proceedings; 2026, Vol. 3426 Issue 1, p1-21, 21p |
| Θεματικοί όροι: | Ensemble learning, Feature selection, Defect tracking (Computer software development), Data quality, Machine learning, Software reliability |
| Περίληψη: | Detecting software defects in large-scale systems is critical to minimizing failures and ensuring software reliability, as emphasized by the Standish Group's Chaos Report. In sectors like finance, healthcare, aerospace, and enterprise software, unnoticed defects can result in security vulnerabilities, system malfunctions, and substantial economic setbacks, highlighting the necessity for effective defect prediction models. The challenge is compounded by imbalanced datasets and the need for effective feature selection techniques. This study introduces a six-stage frame-work designed to address these challenges: data preprocessing, handling class imbalance, feature selection, ensemble classifier implementation, hyperparameter tuning, and performance evaluation. The framework integrates Mutual Information, Recursive Feature Elimination, and Principal Component Analysis (PCA), incorporating Polynomial Features and scaling to reduce noise. The methodology employs Intelligent Heterogeneous Ensemble Learning (IHEL), combining "Stacking and Voting" with base classifiers like SVM, Logistic Regression, XGBoost, CatBoost, and Gradient Boosting, and optimizing the meta-classifier, Random Forest, through GridSearchCV. Tests conducted on PROMISE datasets (Pc1, Cm1) demonstrate significant improvements in metrics crucial for imbalanced classes, such as Positive Predicted Value, True Positive Rate, Precision-Recall Harmonic Mean, and Sensitivity. The results highlight that the proposed framework outperforms individual classifiers and traditional ensemble models, providing a robust and efficient solution for defect prediction in imbalanced scenarios. This study concludes that adopting this integrated approach can effectively enhance defect detection capabilities in software engineering. [ABSTRACT FROM AUTHOR] |
| Copyright of AIP Conference Proceedings is the property of American Institute of Physics 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 | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 194305349 RelevancyScore: 1097 AccessLevel: 6 PubType: Conference PubTypeId: conference PreciseRelevancyScore: 1097.4189453125 |
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| Items | – Name: Title Label: Title Group: Ti Data: Integrated frameworks for intelligent detection of software faults. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sharma%2C+Tarunim%22">Sharma, Tarunim</searchLink><br /><searchLink fieldCode="AR" term="%22Bhaskar%2C+Shalini%22">Bhaskar, Shalini</searchLink><br /><searchLink fieldCode="AR" term="%22Jatain%2C+Aman%22">Jatain, Aman</searchLink><br /><searchLink fieldCode="AR" term="%22Pabreja%2C+Kavita%22">Pabreja, Kavita</searchLink> – Name: TitleSource Label: Source Group: Src Data: AIP Conference Proceedings; 2026, Vol. 3426 Issue 1, p1-21, 21p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Defect+tracking+%28Computer+software+development%29%22">Defect tracking (Computer software development)</searchLink><br /><searchLink fieldCode="DE" term="%22Data+quality%22">Data quality</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Software+reliability%22">Software reliability</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Detecting software defects in large-scale systems is critical to minimizing failures and ensuring software reliability, as emphasized by the Standish Group's Chaos Report. In sectors like finance, healthcare, aerospace, and enterprise software, unnoticed defects can result in security vulnerabilities, system malfunctions, and substantial economic setbacks, highlighting the necessity for effective defect prediction models. The challenge is compounded by imbalanced datasets and the need for effective feature selection techniques. This study introduces a six-stage frame-work designed to address these challenges: data preprocessing, handling class imbalance, feature selection, ensemble classifier implementation, hyperparameter tuning, and performance evaluation. The framework integrates Mutual Information, Recursive Feature Elimination, and Principal Component Analysis (PCA), incorporating Polynomial Features and scaling to reduce noise. The methodology employs Intelligent Heterogeneous Ensemble Learning (IHEL), combining "Stacking and Voting" with base classifiers like SVM, Logistic Regression, XGBoost, CatBoost, and Gradient Boosting, and optimizing the meta-classifier, Random Forest, through GridSearchCV. Tests conducted on PROMISE datasets (Pc1, Cm1) demonstrate significant improvements in metrics crucial for imbalanced classes, such as Positive Predicted Value, True Positive Rate, Precision-Recall Harmonic Mean, and Sensitivity. The results highlight that the proposed framework outperforms individual classifiers and traditional ensemble models, providing a robust and efficient solution for defect prediction in imbalanced scenarios. This study concludes that adopting this integrated approach can effectively enhance defect detection capabilities in software engineering. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of AIP Conference Proceedings is the property of American Institute of Physics 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: 10.1063/5.0328093 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 1 Subjects: – SubjectFull: Ensemble learning Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Defect tracking (Computer software development) Type: general – SubjectFull: Data quality Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Software reliability Type: general Titles: – TitleFull: Integrated frameworks for intelligent detection of software faults. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sharma, Tarunim – PersonEntity: Name: NameFull: Bhaskar, Shalini – PersonEntity: Name: NameFull: Jatain, Aman – PersonEntity: Name: NameFull: Pabreja, Kavita IsPartOfRelationships: – BibEntity: Dates: – D: 04 M: 06 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 0094243X Numbering: – Type: volume Value: 3426 – Type: issue Value: 1 Titles: – TitleFull: AIP Conference Proceedings Type: main |
| ResultId | 1 |