Implementation of Hybrid Particle Swarm Optimization for Optimized Regression Testing

作     者：V.Prakash S.Gopalakrishnan 

作者机构：SASTRA Deemed UniversityKumbakonamTamilnadu613401India 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第36卷第6期

页      面：2575-2590页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Equivalent mutation fault diagnosis hospital information system software test case optimization tournament winner genetic algorithm 

摘      要：Software test case optimization improves the efficiency of the software by proper structure and reduces the fault in the *** existing research applies various optimization methods such as Genetic Algorithm,Crow Search Algorithm,Ant Colony Optimization,etc.,for test case *** existing methods have limitations of lower efficiency in fault diagnosis,higher computa-tional time,and high memory *** existing methods have lower effi-ciency in software test case optimization when the number of test cases is *** research proposes the Tournament Winner Genetic Algorithm(TW-GA)method to improve the efficiency of software test case *** Information System(HIS)software was used to evaluate TW-GA model perfor-mance in test case *** tournament Winner in the proposed method selects the instances with the best fitness values and increases the exploitation of the search to find the optimal *** TW-GA method has higher exploita-tion that helps to find the mutant and equivalent mutation that significantly increases fault diagnosis in the *** TW-GA method discards the infor-mation with a lower fitness value that reduces the computational time and mem-ory *** TW-GA method requires 5.47 s and the MOCSFO method requires 30 s for software test case optimization.