A cross‐project defect prediction method based on multi‐adaptation and nuclear norm
Abstract Cross‐project defect prediction (CPDP) is an important research direction in software defect prediction. Traditional CPDP methods based on hand‐crafted features ignore the semantic information in the source code. Existing CPDP methods based on the deep learning model may not fully consider...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-04-01
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| Series: | IET Software |
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| Online Access: | https://doi.org/10.1049/sfw2.12053 |
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| _version_ | 1832546661132402688 |
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| author | Qingan Huang Le Ma Siyu Jiang Guobin Wu Hengjie Song Libiao Jiang Chunyun Zheng |
| author_facet | Qingan Huang Le Ma Siyu Jiang Guobin Wu Hengjie Song Libiao Jiang Chunyun Zheng |
| author_sort | Qingan Huang |
| collection | DOAJ |
| description | Abstract Cross‐project defect prediction (CPDP) is an important research direction in software defect prediction. Traditional CPDP methods based on hand‐crafted features ignore the semantic information in the source code. Existing CPDP methods based on the deep learning model may not fully consider the differences among projects. Additionally, these methods may not accurately classify the samples near the classification boundary. To solve these problems, the authors propose a model based on multi‐adaptation and nuclear norm (MANN) to deal with samples in projects. The feature of samples were embedded into the multi‐core Hilbert space for distribution and the multi‐kernel maximum mean discrepancy method was utilised to reduce differences among projects. More importantly, the nuclear norm module was constructed, which improved the discriminability and diversity of the target sample by calculating and maximizing the nuclear norm of the target sample in the process of domain adaptation, thus improving the performance of MANN. Finally, extensive experiments were conducted on 11 sizeable open‐source projects. The results indicate that the proposed method exceeds the state of the art under the widely used metrics. |
| format | Article |
| id | doaj-art-5346c562161b4821ba5fc6ffccbd0b2d |
| institution | Kabale University |
| issn | 1751-8806 1751-8814 |
| language | English |
| publishDate | 2022-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Software |
| spelling | doaj-art-5346c562161b4821ba5fc6ffccbd0b2d2025-02-03T06:47:36ZengWileyIET Software1751-88061751-88142022-04-0116220021310.1049/sfw2.12053A cross‐project defect prediction method based on multi‐adaptation and nuclear normQingan Huang0Le Ma1Siyu Jiang2Guobin Wu3Hengjie Song4Libiao Jiang5Chunyun Zheng6School of Software Engineering South China University of Technology Guangzhou ChinaGuangzhou City University of Technology Guangzhou ChinaGuangzhou Key Laboratory of Multilingual Intelligent Processing School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou ChinaSchool of Software Engineering South China University of Technology Guangzhou ChinaSchool of Software Engineering South China University of Technology Guangzhou ChinaGuangzhou City University of Technology Guangzhou ChinaAutomotive Engineering Research Institute Guangzhou Automobile Group Co., Ltd Guangzhou ChinaAbstract Cross‐project defect prediction (CPDP) is an important research direction in software defect prediction. Traditional CPDP methods based on hand‐crafted features ignore the semantic information in the source code. Existing CPDP methods based on the deep learning model may not fully consider the differences among projects. Additionally, these methods may not accurately classify the samples near the classification boundary. To solve these problems, the authors propose a model based on multi‐adaptation and nuclear norm (MANN) to deal with samples in projects. The feature of samples were embedded into the multi‐core Hilbert space for distribution and the multi‐kernel maximum mean discrepancy method was utilised to reduce differences among projects. More importantly, the nuclear norm module was constructed, which improved the discriminability and diversity of the target sample by calculating and maximizing the nuclear norm of the target sample in the process of domain adaptation, thus improving the performance of MANN. Finally, extensive experiments were conducted on 11 sizeable open‐source projects. The results indicate that the proposed method exceeds the state of the art under the widely used metrics.https://doi.org/10.1049/sfw2.12053neural netssoftware qualitysoftware reliabilityunsupervised learning |
| spellingShingle | Qingan Huang Le Ma Siyu Jiang Guobin Wu Hengjie Song Libiao Jiang Chunyun Zheng A cross‐project defect prediction method based on multi‐adaptation and nuclear norm IET Software neural nets software quality software reliability unsupervised learning |
| title | A cross‐project defect prediction method based on multi‐adaptation and nuclear norm |
| title_full | A cross‐project defect prediction method based on multi‐adaptation and nuclear norm |
| title_fullStr | A cross‐project defect prediction method based on multi‐adaptation and nuclear norm |
| title_full_unstemmed | A cross‐project defect prediction method based on multi‐adaptation and nuclear norm |
| title_short | A cross‐project defect prediction method based on multi‐adaptation and nuclear norm |
| title_sort | cross project defect prediction method based on multi adaptation and nuclear norm |
| topic | neural nets software quality software reliability unsupervised learning |
| url | https://doi.org/10.1049/sfw2.12053 |
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