Multiview Transfer Learning for Software Defect Prediction
Most software defect prediction models usually assume that enough historical training instances with labels are available. Additionally, the training data and the predicted instances should share the same features to ensure the prediction accuracy. However, in practice, there are many datasets with...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8600320/ |
| _version_ | 1818416100819337216 |
|---|---|
| author | Jinyin Chen Yitao Yang Keke Hu Qi Xuan Yi Liu Chao Yang |
| author_facet | Jinyin Chen Yitao Yang Keke Hu Qi Xuan Yi Liu Chao Yang |
| author_sort | Jinyin Chen |
| collection | DOAJ |
| description | Most software defect prediction models usually assume that enough historical training instances with labels are available. Additionally, the training data and the predicted instances should share the same features to ensure the prediction accuracy. However, in practice, there are many datasets with different granularities containing information in different dimensions. Therefore, it is valuable to effectively use the small scale and different dimensions of data as training instances to improve the prediction performance of the model. We propose a heterogeneous data orienting multiview transfer learning for software defect prediction, denoted as MTDP, which can achieve different dimensions and granularities features to automatically learn labels through neural network models. With this multiview transfer method, lots of training instances are provided for software defect prediction model to ensure the effectiveness of training labels. The proposed MTDP method has four main stages: 1) build heterogeneous transfer models; 2) transfer heterogeneous instances to generate quasi-real instances; 3) label quasi-real instances through co-training and then expand the training set; and (4) construct improved software defect prediction models. The experimental results show that the quasi-real instances have similar effects compared with real instances. Moreover, the software defect prediction performance can be improved by introducing the quasi-real instances into the training dataset. |
| first_indexed | 2024-12-14T11:45:31Z |
| format | Article |
| id | doaj.art-2f6b9b7f717b461a95d254d19bad50c8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:45:31Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2f6b9b7f717b461a95d254d19bad50c82022-12-21T23:02:36ZengIEEEIEEE Access2169-35362019-01-0178901891610.1109/ACCESS.2018.28907338600320Multiview Transfer Learning for Software Defect PredictionJinyin Chen0Yitao Yang1Keke Hu2Qi Xuan3Yi Liu4https://orcid.org/0000-0002-4066-689XChao Yang5College of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaInstitute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou, ChinaInstitute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou, ChinaMost software defect prediction models usually assume that enough historical training instances with labels are available. Additionally, the training data and the predicted instances should share the same features to ensure the prediction accuracy. However, in practice, there are many datasets with different granularities containing information in different dimensions. Therefore, it is valuable to effectively use the small scale and different dimensions of data as training instances to improve the prediction performance of the model. We propose a heterogeneous data orienting multiview transfer learning for software defect prediction, denoted as MTDP, which can achieve different dimensions and granularities features to automatically learn labels through neural network models. With this multiview transfer method, lots of training instances are provided for software defect prediction model to ensure the effectiveness of training labels. The proposed MTDP method has four main stages: 1) build heterogeneous transfer models; 2) transfer heterogeneous instances to generate quasi-real instances; 3) label quasi-real instances through co-training and then expand the training set; and (4) construct improved software defect prediction models. The experimental results show that the quasi-real instances have similar effects compared with real instances. Moreover, the software defect prediction performance can be improved by introducing the quasi-real instances into the training dataset.https://ieeexplore.ieee.org/document/8600320/Multiview transfer learningsoftware defect predictionactive learningneural network |
| spellingShingle | Jinyin Chen Yitao Yang Keke Hu Qi Xuan Yi Liu Chao Yang Multiview Transfer Learning for Software Defect Prediction IEEE Access Multiview transfer learning software defect prediction active learning neural network |
| title | Multiview Transfer Learning for Software Defect Prediction |
| title_full | Multiview Transfer Learning for Software Defect Prediction |
| title_fullStr | Multiview Transfer Learning for Software Defect Prediction |
| title_full_unstemmed | Multiview Transfer Learning for Software Defect Prediction |
| title_short | Multiview Transfer Learning for Software Defect Prediction |
| title_sort | multiview transfer learning for software defect prediction |
| topic | Multiview transfer learning software defect prediction active learning neural network |
| url | https://ieeexplore.ieee.org/document/8600320/ |
| work_keys_str_mv | AT jinyinchen multiviewtransferlearningforsoftwaredefectprediction AT yitaoyang multiviewtransferlearningforsoftwaredefectprediction AT kekehu multiviewtransferlearningforsoftwaredefectprediction AT qixuan multiviewtransferlearningforsoftwaredefectprediction AT yiliu multiviewtransferlearningforsoftwaredefectprediction AT chaoyang multiviewtransferlearningforsoftwaredefectprediction |