Empirical Studies on Feature Selection for Software Fault Prediction

摘要：

　　Classification based software fault prediction methods aim to classify the modules into either fault-prone or non-faultprone.Feature selection is a preprocess step used to improve the data quality.However most of previous research mainly focus on feature relevance analysis,there is little work focusing on feature redundancy analysis.Therefore we propose a two-stage framework for feature selection to solve this issue.In particular,during the feature relevance phase,we adopt three different relevance measures to obtain the relevant feature subset.Then during the feature redundancy analysis phase,we use a cluster-based method to eliminate redundant features.To verify the effectiveness of our proposed framework,we choose typical real-world software projects,including Eclipse projects and NASA software project KC1.Final empirical result shows the effectiveness of our proposed framework.

关键词： Software Fault Prediction Feature Selection Relevance Analysis Redundancy Analysis

作者: Jiaqiang Chen Shulong Liu Xiang Chen Qing Gu Daoxu Chen

作者单位: State Key Laboratory for Novel Software Technology Nanjing University,Nanjing,China State Key Laboratory for Novel Software Technology Nanjing University,Nanjing,China;School of Comput

会议类型: 国际会议

会议名称: 第五届亚太网构软件研讨会

会议地点: 长沙

会议语种:英文

页码: 163-166

在线出版日期: 2013-10-23（万方平台首次上网日期，不代表论文的发表时间）