面向软件缺陷数据聚类分析的数据降维处理方法  

Data Dimensionality Reduction Method Oriented to Software Defect Data Clustering Analysis

作　　者：万琳[1] 范秋灵[1] 

机构地区：[1]装甲兵工程学院信息工程系,北京100072

出　　处：《计算机系统应用》2015年第3期207-213,共7页Computer Systems & Applications

摘　　要：面向软件缺陷数据的聚类分析就是按照一定的准则将不同的软件缺陷数据对象划分为多个类,使得类内的缺陷数据相似,类间的缺陷数据相异,其意义在于发现软件缺陷的分布规律,有针对性地制定测试方案,优化测试过程.针对传统K-Means方法聚类结果依赖样本初始空间分布的问题,提出一种基于PSO算法的数据降维处理方法 DRPS.仿真实验表明,经过该方法降维处理后数据的聚类准确率及聚类质量都有了一定程度的提高.Clustering analysis oriented to software defect data is dividing different software defect data to different clusters according to some criterion. The result of clustering is that defect data in the same cluster is similar while defect data in different clusters is different. It is significant to find the distribution law of software defect make testing scheme and optimize testing process. Due to that the clustering results of K-Means is dependent on distribution of samples, a data dimensionality reduction method based on PSO is proposed. Simulation experiment shows that the clustering accuracy and quality are improved to some extent.

关 键 词：软件缺陷 模糊等价矩阵 聚类分析 粒子群优化 数据降维 

分 类 号：TP311.53[自动化与计算机技术—计算机软件与理论] TP311.13[自动化与计算机技术—计算机科学与技术]