融合聚类算法和缺陷预测的测试用例优先排序方法  

Test Case Prioritization Combining Clustering Approach and Fault Prediction

作　　者：肖蕾 陈荣赏[1,4] 缪淮扣 洪煜[5] XIAO Lei;CHEN Rong-shang;MIAO Huai-kou;HONG Yu(College of Computer and Information Engineering,Xiamen University of Technology,Xiamen,Fujian 361024,China;School of Computer Engineering and Science,Shanghai University,Shanghai 200444,China;Shanghai Key Laboratory of Computer Software Testing&Evaluating,Shanghai 201112,China;Engineering Research Center for Software Testing and Evaluation of Fujian Province,Xiamen,Fujian 361000,China;Fujian Yifei Information Technology Co.,Ltd.,Xiamen,Fujian 361000,China)

机构地区：[1]厦门理工学院计算机与信息工程学院,福建厦门361024 [2]上海大学计算机工程与科学学院,上海200444 [3]上海市计算机软件评测重点实验室,上海201112 [4]福建省软件评测工程技术研究中心,福建厦门361000 [5]福建壹飞信息科技有限公司,福建厦门361000

出　　处：《计算机科学》2021年第5期99-108,共10页Computer Science

基　　金：国家自然科学基金(61572306);福建省软件评测工程技术研究中心资助项目(ST2019002);2020年度重庆市出版专项资金资助项目。

摘　　要：持续集成环境下,软件快速更新加快了回归测试执行的频率,但缺陷快速反馈的需求对回归测试又提出了更高要求。测试用例优先排序技术研究测试用例的重要性,通常将缺陷探测能力强的测试用例优先执行,使其提早发现软件缺陷,其可解决持续集成环境下的快速反馈需求。缺陷预测技术可通过被测系统代码特征和历史缺陷来预估信息预测软件在新版本中发现缺陷的可能性,传统基于聚类的测试用例优先排序方法大多未考虑不同类簇数和特征子集对聚类结果的影响。文中将缺陷预测应用到聚类优先排序方法,构建测试用例和代码关联矩阵,对测试用例进行聚类分析,结合缺陷预测结果和最大最小距离策略指导簇间和簇内排序。通过实验验证发现,类簇数和聚类特征子集选择对排序效果有一定影响,当未能获取最佳类簇数和特征子集时,相比单一的聚类优先排序方法,所提方法可更有效地提高回归测试效率。The rapid delivery of software leads to the frequent execution of regression testing.The higher efficiency of regression testing is required for the quick fault-feedback in Continuous integration(CI).The goal of test case prioritization(TCP)approach is that the test cases with the higher fault detection rate are preferentially executed.Therefore,TCP approach meets the requirement of quick fault-feedback in CI.The fault prediction approach can predict the failed probability in the new version using the code feature and the historical failure information.The choice of the number of cluster and the feature subset are not considered in the traditional clustering TCP approaches.This paper proposes a test case prioritization method combining clustering approach and fault prediction,which fisrtly identifies the correlation between the test cases and the codes,then divides the test cases into the different clusters,lastly implements the inter-cluster and intra-cluster prioritization on the guidance of the fault prediction and the maximum and minimum distance strategy.The experimental results verify that the efficiency of prioritization is influenced by the choice of the number of cluster and the feature subset.If the best clustering number and the feature subset are not required,the proposed approach is superior to the traditional clustering TCP approaches.

关 键 词：回归测试 测试用例优先排序 缺陷预测 聚类分析 最佳类簇数 特征子集 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]