{"title":"SOFTWARE TESTING USING GENETIC ALGORITHMS","authors":"Akshat Sharma, Rishon Patani, A. Aggarwal","doi":"10.5121/IJCSES.2016.7203","DOIUrl":null,"url":null,"abstract":"This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in software testing. For several years researchers have proposed several methods for generating test data which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test methods which will be having different parameters to automate the structural-oriented test data generation on the basis of internal program structure. The factors discovered are used in evaluating the fitness function of Genetic algorithm for selecting the best possible Test method. These methods take the test populations as an input and then evaluate the test cases for that program. This integration will help in improving the overall performance of genetic algorithm in search space exploration and exploitation fields with better convergence rate.","PeriodicalId":415526,"journal":{"name":"International Journal of Computer Science & Engineering Survey","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"37","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computer Science & Engineering Survey","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/IJCSES.2016.7203","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 37
Abstract
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in software testing. For several years researchers have proposed several methods for generating test data which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test methods which will be having different parameters to automate the structural-oriented test data generation on the basis of internal program structure. The factors discovered are used in evaluating the fitness function of Genetic algorithm for selecting the best possible Test method. These methods take the test populations as an input and then evaluate the test cases for that program. This integration will help in improving the overall performance of genetic algorithm in search space exploration and exploitation fields with better convergence rate.