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Volume 32 Issue 6
November  2017
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Chao Ni, Wang-Shu Liu, Xiang Chen, Qing Gu, Dao-Xu Chen, Qi-Guo Huang. A Cluster Based Feature Selection Method for Cross-Project Software Defect Prediction[J]. Journal of Computer Science and Technology, 2017, 32(6): 1090-1107. DOI: 10.1007/s11390-017-1785-0
Citation: Chao Ni, Wang-Shu Liu, Xiang Chen, Qing Gu, Dao-Xu Chen, Qi-Guo Huang. A Cluster Based Feature Selection Method for Cross-Project Software Defect Prediction[J]. Journal of Computer Science and Technology, 2017, 32(6): 1090-1107. DOI: 10.1007/s11390-017-1785-0
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A Cluster Based Feature Selection Method for Cross-Project Software Defect Prediction

  • 1 State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China;
    2 School of Computer Science and Technology, Nantong University, Nantong 226019, China

Funds: This work is supported in part by the National Natural Science Foundation of China under Grant Nos. 61373012, 91218302, 61321491 and 61202006, the Collaborative Innovation Center of Novel Software Technology and Industrialization, the Open Project of State Key Laboratory for Novel Software Technology at Nanjing University under Grant No. KFKT2016B18, and the National Basic Research 973 Program of China under Grant No. 2009CB320705.
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  • Author Bio:

    Chao Ni received his B.S.degree in computer science from Nantong University,Nantong,in 2014.Then he received his M.S.degree in computer science from Nanjing University,Nanjing,in 2017.Now he is a Ph.D.candidate of State Key Laboratory for Novel Software Technology and the Department of Computer Science and Technology,Nanjing University,Nanjing.His research interests are mainly in software defect prediction and machine learning.

  • Corresponding author:

    Qing Gu E-mail:guq@nju.edu.cn

  • Received Date: April 20, 2017
  • Revised Date: September 26, 2017
  • Published Date: November 04, 2017
    Abstract
  • Cross-project defect prediction (CPDP) uses the labeled data from external source software projects to compensate the shortage of useful data in the target project, in order to build a meaningful classification model. However, the distribution gap between software features extracted from the source and the target projects may be too large to make the mixed data useful for training. In this paper, we propose a cluster-based novel method FeSCH (Feature Selection Using Clusters of Hybrid-Data) to alleviate the distribution differences by feature selection. FeSCH includes two phases. The feature clustering phase clusters features using a density-based clustering method, and the feature selection phase selects features from each cluster using a ranking strategy. For CPDP, we design three different heuristic ranking strategies in the second phase. To investigate the prediction performance of FeSCH, we design experiments based on real-world software projects, and study the effects of design options in FeSCH (such as ranking strategy, feature selection ratio, and classifiers). The experimental results prove the effectiveness of FeSCH. Firstly, compared with the state-of-the-art baseline methods, FeSCH achieves better performance and its performance is less affected by the classifiers used. Secondly, FeSCH enhances the performance by effectively selecting features across feature categories, and provides guidelines for selecting useful features for defect prediction.
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