计算机应用研究2017，Vol.34Issue(4)：1105-1108,1119,5.DOI:10.3969/j.issn.1001-3695.2017.04.034
SBFS:基于搜索的软件缺陷预测特征选择框架
SBFS:search based feature selection framework for software defect prediction
摘要
Abstract
During the process of gathering defect prediction datasets,the issue of curse of dimensionality may exist in these datasets when considering different metrics based on code complexity or development process.Motivated by the idea of search based software engineering,this paper proposed a novel search based wrapper feature selection framework SBFS.In implementing this framework,it first used SMOTE approach to alleviate the issue of class imbalance,then used a genetic algorithm based feature selection method to select the optimal feature subset based on the training set.In empirical studies,it used NASA dataset as the subjects.Then it chose some classical baseline methods,such as forward search based wrapper feature selection method FW,backward search based wrapper feature selection method BW,and no feature selection method Origin.Finally results show that SBFS is no worse than Origin in 90％ of cases,is no worse than BW in 82.3％ of cases,and is no worse than FW in 69.3％ of cases.Furthermore,when using decision tree classifier,using SMOTE can improve the model performance in 71％ of cases.However when using Naive Bayes classifier or Logistic regression classifier,using SMOTE can only improve the model performance in 47％ and 43％ of cases respectively.关键词
软件缺陷预测/特征选择/基于搜索的软件工程/类不平衡学习Key words
software defect prediction/feature selection/search based software engineering/class imbalance learning分类
信息技术与安全科学引用本文复制引用
陈翔,陆凌姣,吉人,魏世鑫..SBFS:基于搜索的软件缺陷预测特征选择框架[J].计算机应用研究,2017,34(4):1105-1108,1119,5.基金项目
国家自然科学基金资助项目(61202006) （61202006）
江苏省大学生创新训练计划资助项(201610304090X) （201610304090X）
