基于多次随机欠采样和POSS方法的软件缺陷检测

doi:10.6040/j.issn.1672-3961.0.2016.304

山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (1): 15-21.doi: 10.6040/j.issn.1672-3961.0.2016.304

基于多次随机欠采样和POSS方法的软件缺陷检测

方昊,李云^*

南京邮电大学计算机学院, 江苏南京 210003

收稿日期:2016-07-22 出版日期:2017-02-20 发布日期:2016-07-22
通讯作者: 李云(1974— ),男,安徽望江人,教授,博士,主要研究方向为机器学习与模式识别.E-mail:liyun@njupt.edu.cn E-mail:15150662912@163.com
作者简介:方昊(1989— ),男,江苏宿迁人,硕士研究生,主要研究方向为特征选择.E-mail:15150662912@163.com
基金资助:
江苏省自然科学基金资助项目(BK20131378,BK20140885);广西高校云计算与复杂系统重点实验室资助项目(15206)

Random undersampling and POSS method for software defect prediction

FANG Hao, LI Yun^*

College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, Jiangsu, China

Received:2016-07-22 Online:2017-02-20 Published:2016-07-22

摘要/Abstract

摘要： 为了解决因软件缺陷数据存在数据不平衡问题限制了分类器的性能,将POSS(pareto optimization for subset selection)特征选择算法和随机欠采样技术引入到软件缺陷检测中,并利用支持向量机(support vector machine, SVM)构建预测模型。试验结果表明,通过多次随机欠采样可以有效地解决软件缺陷数据不平衡问题,同时使用POSS方法对目标子集进行双向优化,从而提高分类的准确率,其结果要优于Relief、Fisher、MI(mutual information)特征选择算法。

关键词: 软件缺陷检测, 不平衡性, 数据采样, 特征选择

Abstract: In order to solve the problem of imbalance distribution in software defect prediction, POSS(pareto optimization for subset selection)feature selection and random undersampling was applied in this paper, and SVM was used to build the prediction model. The experimental results showed that the problem could be solved effectively by using multiple random undersampling, and the POSS method was treated subset selection as a bi-objective optimization, which could improve the accuracy of classification, the effectiveness of proposed method was verified by comparing with Relief、Fisher、MI(mutual information).

Key words: class imbalance, data sampling, feature selection, software defect prediction

中图分类号:

TP391

方昊,李云. 基于多次随机欠采样和POSS方法的软件缺陷检测[J]. 山东大学学报(工学版), 2017, 47(1): 15-21.

FANG Hao, LI Yun. Random undersampling and POSS method for software defect prediction[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(1): 15-21.

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基于多次随机欠采样和POSS方法的软件缺陷检测

Random undersampling and POSS method for software defect prediction

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