基于半监督聚类方法的测试用例选择技术

doi:10.11896/j.issn.1002-137X.2018.01.044

摘要/Abstract

摘要： 回归测试的目的是保证软件修改后没有引入新的错误。但是随着软件的演化,回归测试用例集不断增大,为了控制成本,回归测试用例选择技术应运而生。近年来,聚类分析技术被运用到回归测试用例选择问题中。将半监督学习引入到聚类技术中,提出了判别型半监督K-means聚类方法(Discriminative Semi-supervised K-means clustering Method,DSKM)。该方法从回归测试的历史执行记录中挖掘出隐藏的成对约束信息,同时利用大量的无标签样本和少量的有标签样本进行学习,优化聚类的结果,并进一步优化测试用例选择的结果。实验表明,相对于Constrained-Kmeans方法和SSKM方法,DSKM方法能够更好地提高约简率并保持覆盖率。

关键词: 回归测试,测试用例选择,K-means算法,成对约束,线性判别分析,半监督聚类

Abstract: The purpose of regression testing is to ensure that no new faults are introduced into software after modifications.The case of regression test is increasing with the evolution of software,the software of so test selection techniques are used to control costs.In recent years,cluster analysis techniques are applied to test selection problem.We proposed a novel method called discriminative semi-supervised K-means clustering method (DSKM),which introduces semi-supervised learning clustering technology.Through DSKM,hidden pairwise constraints information is mined from the test execution history.By taking advantage of a large number of unlabeled samples and a small amount of labeled samples,DSKM can optimize the results of the cluster,and further optimize test case selection results.Experiment shows that compared with Constrained-Kmeans algorithm and SSKM method,DSKM is more effective.

Key words: Regression testing,Test case selection,K-means algorithm,Pairwise constraints,Linear discriminant analysis,Semi-supervised clustering

程雪梅,杨秋辉,翟宇鹏,陈伟. 基于半监督聚类方法的测试用例选择技术[J]. 计算机科学, 2018, 45(1): 249-254. https://doi.org/10.11896/j.issn.1002-137X.2018.01.044

CHENG Xue-mei, YANG Qiu-hui, ZHAI Yu-peng and CHEN Wei. Test Case Selection Technique Based on Semi-supervised Clustering Method[J]. Computer Science, 2018, 45(1): 249-254. https://doi.org/10.11896/j.issn.1002-137X.2018.01.044

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