An Improved K-means Algorithm for Test Case Optimization

被引：1
|
作者：
Tan, Tian-Tian [1 ]
Wang, Bao-Sheng [1 ]
Tang, Yong [1 ]
Zhou, Xu [1 ]
机构：
[1] Natl Univ Def Technol, Dept Comp, Changsha, Peoples R China
来源：
2019 IEEE 4TH INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION SYSTEMS (ICCCS 2019) | 2019年
关键词：
Software Test; K-means Algorithm; Degree of Membership Function;
D O I：
10.1109/ccoms.2019.8821687
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to optimize the effectiveness and efficiency of software test cases, this paper proposed an improved K-means algorithm for test case optimization, introduced Degree of Membership Function to improve K-Means algorithm to design a fuzzy clustering method, and combined the test requirements set, extracted test cases from each cluster, found similar test cases as more as possible. Experimental results showed that this algorithm can minimize the redundant test case set, keep the widest coverage at the same time, and has higher effectiveness and efficiency.
引用
收藏
页码：169 / 172
页数：4
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