Boosting Software Fault Prediction: Addressing Class Imbalance With Enhanced Ensemble Learning
Software fault prediction (SFP) is a crucial aspect of software engineering, aiding in the early identification of potential defects. This proactive approach significantly contributes to enhancing software quality and reliability. However, a common challenge in SFP is class imbalance (CI). Ensemble...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2024/2959582 |
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| author | Hanan Sharif Alsorory Mohammad Alshraideh |
| author_facet | Hanan Sharif Alsorory Mohammad Alshraideh |
| author_sort | Hanan Sharif Alsorory |
| collection | DOAJ |
| description | Software fault prediction (SFP) is a crucial aspect of software engineering, aiding in the early identification of potential defects. This proactive approach significantly contributes to enhancing software quality and reliability. However, a common challenge in SFP is class imbalance (CI). Ensemble learning (EL) is a powerful strategy for refining SFP models in object-oriented systems with imbalanced data and improving sensitivity to minority classes. This study aimed to improve the effectiveness of ensemble classes in SFP within object-oriented systems, tackling the challenges associated with imbalanced data. It focuses on enhancing the performance of three ensemble classifiers, BalancedBagging, RUSBoost, and EasyEnsemble, explicitly designed for imbalanced datasets. In Enhanced_BalancedBagging (E_BB) and ROSBoost, random undersampling (RUS) is substituted with random oversampling (ROS). Meanwhile, Enhanced_EasyEnsemble (E_EE) replaces RUS with ROS and AdaBoost with XGBoost. The experimental results demonstrate the superior performance of E_BB, ROSBoost, and E_EE over their base models, achieving the highest F-measure, balanced accuracy, and AUC. Statistical tests, such as the Wilcoxon signed-rank test, provide robust support for the enhanced models, highlighting their practical significance through substantial improvements in F-measure and AUC, as indicated by low negative rank sums and large effect sizes. |
| format | Article |
| id | doaj-art-ac9d00f04bb446659cf71925720cee80 |
| institution | OA Journals |
| issn | 1687-9732 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-ac9d00f04bb446659cf71925720cee802025-08-20T02:20:22ZengWileyApplied Computational Intelligence and Soft Computing1687-97322024-01-01202410.1155/2024/2959582Boosting Software Fault Prediction: Addressing Class Imbalance With Enhanced Ensemble LearningHanan Sharif Alsorory0Mohammad Alshraideh1Computer Science DepartmentArtificial Intelligence DepartmentSoftware fault prediction (SFP) is a crucial aspect of software engineering, aiding in the early identification of potential defects. This proactive approach significantly contributes to enhancing software quality and reliability. However, a common challenge in SFP is class imbalance (CI). Ensemble learning (EL) is a powerful strategy for refining SFP models in object-oriented systems with imbalanced data and improving sensitivity to minority classes. This study aimed to improve the effectiveness of ensemble classes in SFP within object-oriented systems, tackling the challenges associated with imbalanced data. It focuses on enhancing the performance of three ensemble classifiers, BalancedBagging, RUSBoost, and EasyEnsemble, explicitly designed for imbalanced datasets. In Enhanced_BalancedBagging (E_BB) and ROSBoost, random undersampling (RUS) is substituted with random oversampling (ROS). Meanwhile, Enhanced_EasyEnsemble (E_EE) replaces RUS with ROS and AdaBoost with XGBoost. The experimental results demonstrate the superior performance of E_BB, ROSBoost, and E_EE over their base models, achieving the highest F-measure, balanced accuracy, and AUC. Statistical tests, such as the Wilcoxon signed-rank test, provide robust support for the enhanced models, highlighting their practical significance through substantial improvements in F-measure and AUC, as indicated by low negative rank sums and large effect sizes.http://dx.doi.org/10.1155/2024/2959582 |
| spellingShingle | Hanan Sharif Alsorory Mohammad Alshraideh Boosting Software Fault Prediction: Addressing Class Imbalance With Enhanced Ensemble Learning Applied Computational Intelligence and Soft Computing |
| title | Boosting Software Fault Prediction: Addressing Class Imbalance With Enhanced Ensemble Learning |
| title_full | Boosting Software Fault Prediction: Addressing Class Imbalance With Enhanced Ensemble Learning |
| title_fullStr | Boosting Software Fault Prediction: Addressing Class Imbalance With Enhanced Ensemble Learning |
| title_full_unstemmed | Boosting Software Fault Prediction: Addressing Class Imbalance With Enhanced Ensemble Learning |
| title_short | Boosting Software Fault Prediction: Addressing Class Imbalance With Enhanced Ensemble Learning |
| title_sort | boosting software fault prediction addressing class imbalance with enhanced ensemble learning |
| url | http://dx.doi.org/10.1155/2024/2959582 |
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