Software Defect Prediction Using an Intelligent Ensemble-Based Model
Software defect prediction plays a crucial role in enhancing software quality while achieving cost savings in testing. Its primary objective is to identify and send only defective modules to the testing stage. This research introduces an intelligent ensemble-based software defect prediction model th...
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Institute of Electrical and Electronics Engineers Inc.
2024
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2-s2.0-85183952438 Ali M.; Mazhar T.; Arif Y.; Al-Otaibi S.; Ghadi Y.Y.; Shahzad T.; Khan M.A.; Hamam H. Software Defect Prediction Using an Intelligent Ensemble-Based Model 2024 IEEE Access 12 10.1109/ACCESS.2024.3358201 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183952438&doi=10.1109%2fACCESS.2024.3358201&partnerID=40&md5=5ad1900c624b143fd133e72b5b14c0ff Software defect prediction plays a crucial role in enhancing software quality while achieving cost savings in testing. Its primary objective is to identify and send only defective modules to the testing stage. This research introduces an intelligent ensemble-based software defect prediction model that combines diverse classifiers. The proposed model employs a two-stage prediction process to detect defective modules. In the first stage, four supervised machine learning algorithms are employed: Random Forest, Support Vector Machine, Naïve Bayes, and Artificial Neural Network. These algorithms are optimized through iterative parameter optimization to achieve the highest accuracy possible. In the second stage, the predictive accuracy of the individual classifiers is integrated into a voting ensemble to make the final predictions. This ensemble approach further improves the accuracy and reliability of the defect predictions. Seven historical defect datasets from the NASA MDP repository, namely CM1, JM1, MC2, MW1, PC1, PC3, and PC4, were utilized to implement and evaluate the proposed defect prediction system. The results demonstrate that each dataset's proposed intelligent system achieved remarkable accuracy, outperforming twenty state-of-the-art defect prediction techniques, including base classifiers and ensemble methods. © 2013 IEEE. Institute of Electrical and Electronics Engineers Inc. 21693536 English Article All Open Access |
author |
Ali M.; Mazhar T.; Arif Y.; Al-Otaibi S.; Ghadi Y.Y.; Shahzad T.; Khan M.A.; Hamam H. |
spellingShingle |
Ali M.; Mazhar T.; Arif Y.; Al-Otaibi S.; Ghadi Y.Y.; Shahzad T.; Khan M.A.; Hamam H. Software Defect Prediction Using an Intelligent Ensemble-Based Model |
author_facet |
Ali M.; Mazhar T.; Arif Y.; Al-Otaibi S.; Ghadi Y.Y.; Shahzad T.; Khan M.A.; Hamam H. |
author_sort |
Ali M.; Mazhar T.; Arif Y.; Al-Otaibi S.; Ghadi Y.Y.; Shahzad T.; Khan M.A.; Hamam H. |
title |
Software Defect Prediction Using an Intelligent Ensemble-Based Model |
title_short |
Software Defect Prediction Using an Intelligent Ensemble-Based Model |
title_full |
Software Defect Prediction Using an Intelligent Ensemble-Based Model |
title_fullStr |
Software Defect Prediction Using an Intelligent Ensemble-Based Model |
title_full_unstemmed |
Software Defect Prediction Using an Intelligent Ensemble-Based Model |
title_sort |
Software Defect Prediction Using an Intelligent Ensemble-Based Model |
publishDate |
2024 |
container_title |
IEEE Access |
container_volume |
12 |
container_issue |
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doi_str_mv |
10.1109/ACCESS.2024.3358201 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183952438&doi=10.1109%2fACCESS.2024.3358201&partnerID=40&md5=5ad1900c624b143fd133e72b5b14c0ff |
description |
Software defect prediction plays a crucial role in enhancing software quality while achieving cost savings in testing. Its primary objective is to identify and send only defective modules to the testing stage. This research introduces an intelligent ensemble-based software defect prediction model that combines diverse classifiers. The proposed model employs a two-stage prediction process to detect defective modules. In the first stage, four supervised machine learning algorithms are employed: Random Forest, Support Vector Machine, Naïve Bayes, and Artificial Neural Network. These algorithms are optimized through iterative parameter optimization to achieve the highest accuracy possible. In the second stage, the predictive accuracy of the individual classifiers is integrated into a voting ensemble to make the final predictions. This ensemble approach further improves the accuracy and reliability of the defect predictions. Seven historical defect datasets from the NASA MDP repository, namely CM1, JM1, MC2, MW1, PC1, PC3, and PC4, were utilized to implement and evaluate the proposed defect prediction system. The results demonstrate that each dataset's proposed intelligent system achieved remarkable accuracy, outperforming twenty state-of-the-art defect prediction techniques, including base classifiers and ensemble methods. © 2013 IEEE. |
publisher |
Institute of Electrical and Electronics Engineers Inc. |
issn |
21693536 |
language |
English |
format |
Article |
accesstype |
All Open Access |
record_format |
scopus |
collection |
Scopus |
_version_ |
1818940557245808640 |