摘要
软件测试用例优化是软件测试研究的重要方向,有代表性的测试用例优化是提高软件测试效率的重要手段,也是软件测试领域研究的重点和难点。基于此,文章应用模糊聚类算法对测试用例进行了聚类分析。传统模糊聚类算法普遍依赖于欧式距离,文章则利用自适应马氏距离对模糊聚类算法进行了优化,通过2种算法实现了软件测试约简。实验数据证明,该方法具有一定的有效性和可行性。
Software test case optimization is an important direction in software testing research,and representative test case optimization is an important means to improve software testing efficiency,as well as a key and difficult point in the field of software testing research.Based on this,the article applied fuzzy clustering algorithm to cluster and analyze the test cases.Traditional fuzzy clustering algorithms generally rely on Euclidean distance,while this article optimizes the fuzzy clustering algorithm using adaptive Mahalanobis distance and achieves software testing reduction through two algorithms.The experimental data proves that this method has certain effectiveness and feasibility.
作者
蔡静颖
CAI Jingying(Guangdong Preschool Normal College in Maoming,Maoming,Guangdong 525000,China)
基金
2022广东省普通高校特色创新类项目:基于模糊聚类的测试用例优化方法研究(2022KTSCX353)。
关键词
模糊聚类
马氏距离
软件测试
测试用例约简
fuzzy clustering
Mahalanobis distance
software testing
test cases reduction
