Cross-Project Defect Prediction Method Based on Manifold Feature Transformation
Traditional research methods in software defect prediction use part of the data in the same project to train the defect prediction model and predict the defect label of the remaining part of the data. However, in the practical realm of software development, the software project that needs to be pred...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/13/8/216 |
| _version_ | 1827685184629112832 |
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| author | Yu Zhao Yi Zhu Qiao Yu Xiaoying Chen |
| author_facet | Yu Zhao Yi Zhu Qiao Yu Xiaoying Chen |
| author_sort | Yu Zhao |
| collection | DOAJ |
| description | Traditional research methods in software defect prediction use part of the data in the same project to train the defect prediction model and predict the defect label of the remaining part of the data. However, in the practical realm of software development, the software project that needs to be predicted is generally a brand new software project, and there is not enough labeled data to build a defect prediction model; therefore, traditional methods are no longer applicable. Cross-project defect prediction uses the labeled data of the same type of project similar to the target project to build the defect prediction model, so as to solve the problem of data loss in traditional methods. However, the difference in data distribution between the same type of project and the target project reduces the performance of defect prediction. To solve this problem, this paper proposes a cross-project defect prediction method based on manifold feature transformation. This method transforms the original feature space of the project into a manifold space, then reduces the difference in data distribution of the transformed source project and the transformed target project in the manifold space, and finally uses the transformed source project to train a naive Bayes prediction model with better performance. A comparative experiment was carried out using the Relink dataset and the AEEEM dataset. The experimental results show that compared with the benchmark method and several cross-project defect prediction methods, the proposed method effectively reduces the difference in data distribution between the source project and the target project, and obtains a higher F1 value, which is an indicator commonly used to measure the performance of the two-class model. |
| first_indexed | 2024-03-10T08:48:18Z |
| format | Article |
| id | doaj.art-f2500e586ce2423c99d25655a9aa061f |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T08:48:18Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-f2500e586ce2423c99d25655a9aa061f2023-11-22T07:44:31ZengMDPI AGFuture Internet1999-59032021-08-0113821610.3390/fi13080216Cross-Project Defect Prediction Method Based on Manifold Feature TransformationYu Zhao0Yi Zhu1Qiao Yu2Xiaoying Chen3School of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, ChinaTraditional research methods in software defect prediction use part of the data in the same project to train the defect prediction model and predict the defect label of the remaining part of the data. However, in the practical realm of software development, the software project that needs to be predicted is generally a brand new software project, and there is not enough labeled data to build a defect prediction model; therefore, traditional methods are no longer applicable. Cross-project defect prediction uses the labeled data of the same type of project similar to the target project to build the defect prediction model, so as to solve the problem of data loss in traditional methods. However, the difference in data distribution between the same type of project and the target project reduces the performance of defect prediction. To solve this problem, this paper proposes a cross-project defect prediction method based on manifold feature transformation. This method transforms the original feature space of the project into a manifold space, then reduces the difference in data distribution of the transformed source project and the transformed target project in the manifold space, and finally uses the transformed source project to train a naive Bayes prediction model with better performance. A comparative experiment was carried out using the Relink dataset and the AEEEM dataset. The experimental results show that compared with the benchmark method and several cross-project defect prediction methods, the proposed method effectively reduces the difference in data distribution between the source project and the target project, and obtains a higher F1 value, which is an indicator commonly used to measure the performance of the two-class model.https://www.mdpi.com/1999-5903/13/8/216cross-project defect predictionmanifold feature transformationnaive Bayes prediction modelF1 |
| spellingShingle | Yu Zhao Yi Zhu Qiao Yu Xiaoying Chen Cross-Project Defect Prediction Method Based on Manifold Feature Transformation Future Internet cross-project defect prediction manifold feature transformation naive Bayes prediction model F1 |
| title | Cross-Project Defect Prediction Method Based on Manifold Feature Transformation |
| title_full | Cross-Project Defect Prediction Method Based on Manifold Feature Transformation |
| title_fullStr | Cross-Project Defect Prediction Method Based on Manifold Feature Transformation |
| title_full_unstemmed | Cross-Project Defect Prediction Method Based on Manifold Feature Transformation |
| title_short | Cross-Project Defect Prediction Method Based on Manifold Feature Transformation |
| title_sort | cross project defect prediction method based on manifold feature transformation |
| topic | cross-project defect prediction manifold feature transformation naive Bayes prediction model F1 |
| url | https://www.mdpi.com/1999-5903/13/8/216 |
| work_keys_str_mv | AT yuzhao crossprojectdefectpredictionmethodbasedonmanifoldfeaturetransformation AT yizhu crossprojectdefectpredictionmethodbasedonmanifoldfeaturetransformation AT qiaoyu crossprojectdefectpredictionmethodbasedonmanifoldfeaturetransformation AT xiaoyingchen crossprojectdefectpredictionmethodbasedonmanifoldfeaturetransformation |