KCO: Balancing class distribution in just-in-time software defect prediction using kernel crossover oversampling.
The performance of the defect prediction model by using balanced and imbalanced datasets makes a big impact on the discovery of future defects. Current resampling techniques only address the imbalanced datasets without taking into consideration redundancy and noise inherent to the imbalanced dataset...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0299585&type=printable |
| _version_ | 1797202054789201920 |
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| author | Ahmad Muhaimin Ismail Siti Hafizah Ab Hamid Asmiza Abdul Sani Nur Nasuha Mohd Daud |
| author_facet | Ahmad Muhaimin Ismail Siti Hafizah Ab Hamid Asmiza Abdul Sani Nur Nasuha Mohd Daud |
| author_sort | Ahmad Muhaimin Ismail |
| collection | DOAJ |
| description | The performance of the defect prediction model by using balanced and imbalanced datasets makes a big impact on the discovery of future defects. Current resampling techniques only address the imbalanced datasets without taking into consideration redundancy and noise inherent to the imbalanced datasets. To address the imbalance issue, we propose Kernel Crossover Oversampling (KCO), an oversampling technique based on kernel analysis and crossover interpolation. Specifically, the proposed technique aims to generate balanced datasets by increasing data diversity in order to reduce redundancy and noise. KCO first represents multidimensional features into two-dimensional features by employing Kernel Principal Component Analysis (KPCA). KCO then divides the plotted data distribution by deploying spectral clustering to select the best region for interpolation. Lastly, KCO generates the new defect data by interpolating different data templates within the selected data clusters. According to the prediction evaluation conducted, KCO consistently produced F-scores ranging from 21% to 63% across six datasets, on average. According to the experimental results presented in this study, KCO provides more effective prediction performance than other baseline techniques. The experimental results show that KCO within project and cross project predictions especially consistently achieve higher performance of F-score results. |
| first_indexed | 2024-04-24T07:57:21Z |
| format | Article |
| id | doaj.art-8b2ab860a0ba46b98bc3bf12b684d45b |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-24T07:57:21Z |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-8b2ab860a0ba46b98bc3bf12b684d45b2024-04-18T05:31:46ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-01194e029958510.1371/journal.pone.0299585KCO: Balancing class distribution in just-in-time software defect prediction using kernel crossover oversampling.Ahmad Muhaimin IsmailSiti Hafizah Ab HamidAsmiza Abdul SaniNur Nasuha Mohd DaudThe performance of the defect prediction model by using balanced and imbalanced datasets makes a big impact on the discovery of future defects. Current resampling techniques only address the imbalanced datasets without taking into consideration redundancy and noise inherent to the imbalanced datasets. To address the imbalance issue, we propose Kernel Crossover Oversampling (KCO), an oversampling technique based on kernel analysis and crossover interpolation. Specifically, the proposed technique aims to generate balanced datasets by increasing data diversity in order to reduce redundancy and noise. KCO first represents multidimensional features into two-dimensional features by employing Kernel Principal Component Analysis (KPCA). KCO then divides the plotted data distribution by deploying spectral clustering to select the best region for interpolation. Lastly, KCO generates the new defect data by interpolating different data templates within the selected data clusters. According to the prediction evaluation conducted, KCO consistently produced F-scores ranging from 21% to 63% across six datasets, on average. According to the experimental results presented in this study, KCO provides more effective prediction performance than other baseline techniques. The experimental results show that KCO within project and cross project predictions especially consistently achieve higher performance of F-score results.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0299585&type=printable |
| spellingShingle | Ahmad Muhaimin Ismail Siti Hafizah Ab Hamid Asmiza Abdul Sani Nur Nasuha Mohd Daud KCO: Balancing class distribution in just-in-time software defect prediction using kernel crossover oversampling. PLoS ONE |
| title | KCO: Balancing class distribution in just-in-time software defect prediction using kernel crossover oversampling. |
| title_full | KCO: Balancing class distribution in just-in-time software defect prediction using kernel crossover oversampling. |
| title_fullStr | KCO: Balancing class distribution in just-in-time software defect prediction using kernel crossover oversampling. |
| title_full_unstemmed | KCO: Balancing class distribution in just-in-time software defect prediction using kernel crossover oversampling. |
| title_short | KCO: Balancing class distribution in just-in-time software defect prediction using kernel crossover oversampling. |
| title_sort | kco balancing class distribution in just in time software defect prediction using kernel crossover oversampling |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0299585&type=printable |
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