期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2015
卷号：4
期号：4
页码：1165-1169
出版社：Shri Pannalal Research Institute of Technolgy
摘要：one of the most momentous tasks during software testing is the generation of proper test data. It can also be done by manually and automation process. There are various soft computing Techniques that have been used to automate this task. Here we describe the automatic generation of test data especially for web-based application with the help of Soft computing techniques. These techniques are also called the metaheuristic techniques. The metaheuristic techniques are high -level soft computing strategies that define algorithm framework and technique to find approximate solution for optimization problems. Main purpose is to generating test data automatically with the help of metaheuristic search technique. For this, there are a number of tools available that can be used for automation of test data. But the researchers found, that we can also used some soft computing techniques like genetic algorithm (GA), particle swarm optimization(PSO), simulated annealing, evolutionary algorithm, ant colony optimization, fuzzy logic and so on to generate smart and efficient test data automatically with the help of these soft computing techniques. The existing result have the hybrid Genetic particle swarm technique algorithm(GA-PSO), Hybrid Genetic algorithm and Hill climbing techniques, genetic algorithm(GA) and ant colony algorithm(ACO) and so on. But the combine approach is giving the better result than individual. So we are taking a different combination of soft computing techniques that is GA (genetic algorithm) and fuzzy logic. And then we will compare this combine technique with the earlier proposed hybrid GA-ACO technique.
关键词：software testing; automated test data generation; metaheuristic search techniques; Genetic algorithms; ant colony
