CLASSIFYING FEATURE DESCRIPTION FOR SOFTWARE DEFECT PREDICTION

摘要：

　　To overcome the limitation of numeric feature description of software modules in Software defect prediction,we propose a novel module description technology,which employs the classifying feature,rather than numerical feature to describe the software module.Firstly,we construct independent classifier on each software metric.Then the classifying results in each feature are used to represent every module.We apply two different feature classifier algorithms (based on mean criterion and minimum error rate criterion,respectively) to obtain the classifying feature description of software modules.By using the proposed description technology,the discrimination of each metric is enlarged distinctly.Also,classifying feature description is simpler compared to numeric description,which would accelerate the speed of prediction model learning and reduce the storage space of massive data sets.Experiment results on four NASA data sets (CM1,KC1,KC2 and PC1) demonstrate the effectiveness of classifying feature description,and our algorithms can significantly improve the performance of software defect prediction.

关键词： Feature classifier description binary classification software defect prediction

作者: LING-FENG ZHANG ZHAO-WEI SHANG

作者单位: College of Computer Science, Chongqing University, Chongqing 400030, China

会议类型: 国际会议

会议名称: 2011 International Conference on Wavelet Analysis and Pattern Recognition(2011小波分析与模式识别国际会议)

会议地点: 桂林

会议语种:英文

页码: 138-143

在线出版日期: 2011-07-10（万方平台首次上网日期，不代表论文的发表时间）