Software Defect Prediction based on Geometric Mean for Subspace Learning

摘要：

　　Due to the confusion of fault-prone software modules and non-fault-prone ones,and the limit of traditional mothed such as LDA and PCA,the performance of software defect prediction model is difficult to improve.In this paper,we present GMCRF,a method based on dimensionality reduction technique and conditional random field(CRF)for software defect prediction.In our proposed method,firstly,we leverage geometric mean for subspace learning to choose the best combination of features from data set.Secondly,we propose to apply the best combination of features which is selected by geometric mean-based approach in CRF model.Interestingly,we find that the GMCRF method achieves much better final results than the other approach as shown in the software defect data classification task.

关键词： Software defect prediction high dimensionality conditional random field Geometric Mean

作者: Yan Gao Chunhui Yang Lixin Liang

作者单位: CEPREI,Software Research Center;South China University of Technology Guangzhou,China CEPREI,Software Research Center;Key Laboratory for Performance and Reliability Testing of Foundation Faulty of Compute Science,Guangdong University of Technology,Guangzhou,China

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 225-229

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）