Semi-supervised Heterogeneous Defect Prediction with Open-source Projects on GitHub
| Autor: | Xiao-Yuan Jing, Fei Wu, Xiwei Dong, Ruchuan Wang, Ying Sun, Yanfei Sun |
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| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
Computer science 020207 software engineering 02 engineering and technology computer.software_genre Computer Graphics and Computer-Aided Design ComputingMethodologies_PATTERNRECOGNITION Open source Artificial Intelligence Metric (mathematics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Canonical correlation computer Software |
| Zdroj: | International Journal of Software Engineering and Knowledge Engineering. 31:889-916 |
| ISSN: | 1793-6403 0218-1940 |
| Popis: | The heterogeneous defect prediction (HDP) technique can predict defects in a target company using heterogeneous metric data from external company, which has received substantial research attention. However, existing HDP methods assume that source data is labeled but labeling data is expensive. Semi-supervised defect prediction technique can perform defect prediction with few labeled data. In this paper, we investigate a new problem — semi-supervised HDP (SHDP). To solve this problem, we propose a new approach named cost-sensitive kernel semi-supervised correlation analysis (CKSCA) as a solution of SHDP problem. It introduces unified metric representation and canonical correlation analysis to make the data distributions of different company projects more similar. CKSCA also designs a cost-sensitive kernel semi-supervised discriminant analysis mechanism to utilize the limited labeled data and sufficient real-life unlabeled data from different companies. Besides we collect lots of open-source projects from GitHub website to construct a new large-scale unlabeled dataset called GITHUB dataset. It contains 26,407 modules and is greater than each public project dataset. It has been public online and can be extended continuously. Experiments on the GITHUB dataset and other public datasets indicate that unlabeled GITHUB data can help prediction model improve prediction performance, and CKSCA is effective and efficient for solving SHDP problem. |
| Databáze: | OpenAIRE |
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