Cross-project software defect prediction through multiple learning
Cross-project defect prediction is a method that predicts defects in one software project by using the historical record of another software project. Due to distribution differences and the weak classifier used to build the prediction model, this method has poor prediction performance. Cross-project...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/111549/ http://psasir.upm.edu.my/id/eprint/111549/1/5258-20631-1-PB_beei-new.pdf |
| _version_ | 1848865717227618304 |
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| author | Zakariyau Bala, Yahaya Abdul Samat, Pathiah Yatim Sharif, Khaironi Manshor, Noridayu |
| author_facet | Zakariyau Bala, Yahaya Abdul Samat, Pathiah Yatim Sharif, Khaironi Manshor, Noridayu |
| author_sort | Zakariyau Bala, Yahaya |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Cross-project defect prediction is a method that predicts defects in one software project by using the historical record of another software project. Due to distribution differences and the weak classifier used to build the prediction model, this method has poor prediction performance. Cross-project defect prediction may perform better if distribution differences are reduced, and an appropriate individual classifier is chosen. However, the prediction performance of individual classifiers may be affected in some way by their weaknesses. As a result, in order to boost the accuracy of cross-project defect prediction predictions, this study proposed a strategy that makes use of multiple classifiers and selects attributes that are similar to one another. The proposed method's efficacy was tested using the Relink and AEEEM datasets in an experiment. The findings of the experiments demonstrated that the proposed method produces superior outcomes. To further validate the method, we employed the Wilcoxon sum rank test at 95% significance level. The approach was found to perform significantly better than the baseline methods. |
| first_indexed | 2025-11-15T14:09:08Z |
| format | Article |
| id | upm-111549 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:09:08Z |
| publishDate | 2024 |
| publisher | Institute of Advanced Engineering and Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1115492024-08-09T02:17:15Z http://psasir.upm.edu.my/id/eprint/111549/ Cross-project software defect prediction through multiple learning Zakariyau Bala, Yahaya Abdul Samat, Pathiah Yatim Sharif, Khaironi Manshor, Noridayu Cross-project defect prediction is a method that predicts defects in one software project by using the historical record of another software project. Due to distribution differences and the weak classifier used to build the prediction model, this method has poor prediction performance. Cross-project defect prediction may perform better if distribution differences are reduced, and an appropriate individual classifier is chosen. However, the prediction performance of individual classifiers may be affected in some way by their weaknesses. As a result, in order to boost the accuracy of cross-project defect prediction predictions, this study proposed a strategy that makes use of multiple classifiers and selects attributes that are similar to one another. The proposed method's efficacy was tested using the Relink and AEEEM datasets in an experiment. The findings of the experiments demonstrated that the proposed method produces superior outcomes. To further validate the method, we employed the Wilcoxon sum rank test at 95% significance level. The approach was found to perform significantly better than the baseline methods. Institute of Advanced Engineering and Science 2024 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/111549/1/5258-20631-1-PB_beei-new.pdf Zakariyau Bala, Yahaya and Abdul Samat, Pathiah and Yatim Sharif, Khaironi and Manshor, Noridayu (2024) Cross-project software defect prediction through multiple learning. Bulletin of Electrical Engineering and Informatics, 13 (3). pp. 2027-2035. ISSN 2089-3191; EISSN: 2302-9285 https://beei.org/index.php/EEI/article/view/5258 10.11591/eei.v13i3.5258 |
| spellingShingle | Zakariyau Bala, Yahaya Abdul Samat, Pathiah Yatim Sharif, Khaironi Manshor, Noridayu Cross-project software defect prediction through multiple learning |
| title | Cross-project software defect prediction through multiple
learning |
| title_full | Cross-project software defect prediction through multiple
learning |
| title_fullStr | Cross-project software defect prediction through multiple
learning |
| title_full_unstemmed | Cross-project software defect prediction through multiple
learning |
| title_short | Cross-project software defect prediction through multiple
learning |
| title_sort | cross-project software defect prediction through multiple
learning |
| url | http://psasir.upm.edu.my/id/eprint/111549/ http://psasir.upm.edu.my/id/eprint/111549/ http://psasir.upm.edu.my/id/eprint/111549/ http://psasir.upm.edu.my/id/eprint/111549/1/5258-20631-1-PB_beei-new.pdf |