Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects
Software defect prediction is a critical task in software engineering, enabling organizations to proactively identify and address potential issues in software systems, thereby improving quality and reducing costs. In this study, we evaluated and compared various machine learning models, including lo...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | IET Software |
| Online Access: | http://dx.doi.org/10.1049/sfw2/8832164 |
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| _version_ | 1850182897384816640 |
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| author | Tariq Shahzad Sunawar Khan Tehseen Mazhar Wasim Ahmad Khmaies Ouahada Habib Hamam |
| author_facet | Tariq Shahzad Sunawar Khan Tehseen Mazhar Wasim Ahmad Khmaies Ouahada Habib Hamam |
| author_sort | Tariq Shahzad |
| collection | DOAJ |
| description | Software defect prediction is a critical task in software engineering, enabling organizations to proactively identify and address potential issues in software systems, thereby improving quality and reducing costs. In this study, we evaluated and compared various machine learning models, including logistic regression (LR), random forest (RF), support vector machines (SVMs), convolutional neural networks (CNNs), and eXtreme Gradient Boosting (XGBoost), for software defect prediction using a combination of diverse datasets. The models were trained and tested on preprocessed and feature-selected data, followed by optimization through hyperparameter tuning. Performance evaluation metrics were employed to analyze the results comprehensively, including classification reports, confusion matrices, receiver operating characteristic–area under the curve (ROC-AUC) curves, precision–recall curves, and cumulative gain charts. The results revealed that XGBoost consistently outperformed other models, achieving the highest accuracy, precision, recall, and AUC scores across all metrics. This indicates its robustness and suitability for predicting software defects in real-world applications. |
| format | Article |
| id | doaj-art-e788f199159e4d3e93c74d8dd054da33 |
| institution | OA Journals |
| issn | 1751-8814 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Software |
| spelling | doaj-art-e788f199159e4d3e93c74d8dd054da332025-08-20T02:17:32ZengWileyIET Software1751-88142025-01-01202510.1049/sfw2/8832164Predicting Software Perfection Through Advanced Models to Uncover and Prevent DefectsTariq Shahzad0Sunawar Khan1Tehseen Mazhar2Wasim Ahmad3Khmaies Ouahada4Habib Hamam5Department of Electrical and Electronic Engineering ScienceDepartment of Software EngineeringSchool of Computer ScienceDepartment of ComputingDepartment of Electrical and Electronic Engineering ScienceDepartment of Electrical and Electronic Engineering ScienceSoftware defect prediction is a critical task in software engineering, enabling organizations to proactively identify and address potential issues in software systems, thereby improving quality and reducing costs. In this study, we evaluated and compared various machine learning models, including logistic regression (LR), random forest (RF), support vector machines (SVMs), convolutional neural networks (CNNs), and eXtreme Gradient Boosting (XGBoost), for software defect prediction using a combination of diverse datasets. The models were trained and tested on preprocessed and feature-selected data, followed by optimization through hyperparameter tuning. Performance evaluation metrics were employed to analyze the results comprehensively, including classification reports, confusion matrices, receiver operating characteristic–area under the curve (ROC-AUC) curves, precision–recall curves, and cumulative gain charts. The results revealed that XGBoost consistently outperformed other models, achieving the highest accuracy, precision, recall, and AUC scores across all metrics. This indicates its robustness and suitability for predicting software defects in real-world applications.http://dx.doi.org/10.1049/sfw2/8832164 |
| spellingShingle | Tariq Shahzad Sunawar Khan Tehseen Mazhar Wasim Ahmad Khmaies Ouahada Habib Hamam Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects IET Software |
| title | Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects |
| title_full | Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects |
| title_fullStr | Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects |
| title_full_unstemmed | Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects |
| title_short | Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects |
| title_sort | predicting software perfection through advanced models to uncover and prevent defects |
| url | http://dx.doi.org/10.1049/sfw2/8832164 |
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