文章基本信息

标题：AN EFFICIENT APPROACH FOR TEST SUITE REDUCTION USING K-MEANS CLUSTERING
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作者：MOHAMMED AKOUR ; IMAN AL JARRAH ; AHMAD A. SAIFAN 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：17
出版社：Journal of Theoretical and Applied
摘要：Software testing is the primary approach that is used to test and evaluate software under development. The main goal of testing is to find defects before customers find them out. It is very costly. Therefore, reducing the cost of the test is a big challenge. This paper aims at reducing the cost of the test by eliminating the redundant test cases. Our methodology begins with generating the test cases randomly. The Procedural Language/Structured Query Language (PL/SQL) tool is used to generate test cases from the payroll system database functions. The SPSS software package is used to apply the K-means Clustering algorithm to reduce the test cases. The results reveal that the proposed approach significantly reduces the number of test cases from 776 to 240 while keeping the same coverage.
关键词：Software Vulnerabilities; Software Complexity; Fault prediction; relation; code complexity