Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

作     者：Shweta Singhal Nishtha Jatana Ahmad F Subahi Charu Gupta Osamah Ibrahim Khalaf Youseef Alotaibi 

作者机构：Indira Gandhi Delhi Technical University for WomenDelhi110006India Maharaja Surajmal Institute of TechnologyNew Delhi110058India Department of Computer ScienceUniversity College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia Bhagwan Parshuram Institute of TechnologyRohiniNew Delhi110085India Al-Nahrain UniversityAl-Nahrain Nanorenewable Energy Research CenterBaghdad64074Iraq Department of Computer ScienceCollege of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第74卷第3期

页      面：6755-6774页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research is funded by the Deanship of Scientific Research at Umm Al-Qura University Grant Code:22UQU4281755DSR02 

主　　题：Test case prioritization regression testing test case selection African buffalo optimization nature-inspired meta-heuristic 

摘      要：Software needs modifications and requires revisions *** to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free *** time and cost incurred in this process,need to be reduced by the method of test case selection and *** is observed that many nature-inspired techniques are applied in this *** Buffalo Optimization is one such approach,applied to regression test selection and *** this paper,the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and *** proposed algorithm converges in polynomial time(O(n^(2))).In this paper,the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple *** astounding 62.5%drop in size and a 48.57%drop in the runtime of the original test suite were *** obtained results are compared with Ant Colony *** comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities(80%),and a reduction in the overall execution time and size of the resultant test *** results and analysis,hence,advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization.