{"title":"人工蜂群算法在软件测试中的应用","authors":"S. Dahiya, J. Chhabra, Shakti Kumar","doi":"10.1109/ASWEC.2010.30","DOIUrl":null,"url":null,"abstract":"This paper presents an artificial bee colony based novel search technique for automatic generation of structural software tests. Test cases are symbolically generated by measuring fitness of individuals with the help of branch distance based objective function. Evaluation of the test generator was performed using ten real world programs. Some of these programs had large ranges for input variables. Results show that the new technique is a reasonable alternative for test data generation, but doesn’t perform very well for large inputs and where constraints are having many equality constraints.","PeriodicalId":381789,"journal":{"name":"2010 21st Australian Software Engineering Conference","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"63","resultStr":"{\"title\":\"Application of Artificial Bee Colony Algorithm to Software Testing\",\"authors\":\"S. Dahiya, J. Chhabra, Shakti Kumar\",\"doi\":\"10.1109/ASWEC.2010.30\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an artificial bee colony based novel search technique for automatic generation of structural software tests. Test cases are symbolically generated by measuring fitness of individuals with the help of branch distance based objective function. Evaluation of the test generator was performed using ten real world programs. Some of these programs had large ranges for input variables. Results show that the new technique is a reasonable alternative for test data generation, but doesn’t perform very well for large inputs and where constraints are having many equality constraints.\",\"PeriodicalId\":381789,\"journal\":{\"name\":\"2010 21st Australian Software Engineering Conference\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"63\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 21st Australian Software Engineering Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASWEC.2010.30\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 21st Australian Software Engineering Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASWEC.2010.30","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of Artificial Bee Colony Algorithm to Software Testing
This paper presents an artificial bee colony based novel search technique for automatic generation of structural software tests. Test cases are symbolically generated by measuring fitness of individuals with the help of branch distance based objective function. Evaluation of the test generator was performed using ten real world programs. Some of these programs had large ranges for input variables. Results show that the new technique is a reasonable alternative for test data generation, but doesn’t perform very well for large inputs and where constraints are having many equality constraints.