Title: Improving software performance by automatic test cases through genetic algorithm
Authors: Sudeshna Chakraborty; Vijay Bhanudas Gujar; Tanupriya Choudhury; Bhupesh Kumar Dewangan
Addresses: Department of Computer Science Engineering, Sharda University, Greater Noida, Uttar Pradesh, India ' Department of Computer Science and Engineering, Dhyanshree Institute of Engineering & Technology, Gajawadi, Maharashtra, India ' Informatics Cluster, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India ' Department of Computer Science and Engineering, School of Engineering, O.P. Jindal University, Raigarh, Chhattisgarh, India
Abstract: Software testing is a vital part of software development. One would like to decrease work and get the most out of the number of faults detected. For optimisation problems, test case production is used. Program checking for major problems in regular production trials has a known sufficiency importance factor. Generating test cases automatically will decrease the price and working time considerably. Experiment case information produced without any human interface by using genetic algorithm and random testing is compared with genetic algorithms. Observation is random testing limitations are solved by genetic algorithms. We have implemented these test cases and tested them in real time environments, and the outcomes show good performance.
Keywords: routine test case generation; correspondence class partitioning; arbitrary testing.
International Journal of Computer Applications in Technology, 2022 Vol.68 No.3, pp.228 - 234
Received: 19 Apr 2021
Accepted: 25 May 2021
Published online: 18 Aug 2022 *