{"title":"用遗传算法生成路径测试用例","authors":"Jin-Cherng Lin, Pu-Lin Yeh","doi":"10.1109/ATS.2000.893632","DOIUrl":null,"url":null,"abstract":"Generic algorithms are inspired by Darwin's survival of the fittest theory. This paper discusses a genetic algorithm that can automatically generate test cases to test a selected path. This algorithm takes a selected path as a target and executes sequences of operators iteratively for test cases to evolve. The evolved test case can lead the program execution to achieve the target path. A fitness function named SIMILARITY is defined to determine which test case should survive if the final test case has not been found.","PeriodicalId":403864,"journal":{"name":"Proceedings of the Ninth Asian Test Symposium","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"55","resultStr":"{\"title\":\"Using genetic algorithms for test case generation in path testing\",\"authors\":\"Jin-Cherng Lin, Pu-Lin Yeh\",\"doi\":\"10.1109/ATS.2000.893632\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Generic algorithms are inspired by Darwin's survival of the fittest theory. This paper discusses a genetic algorithm that can automatically generate test cases to test a selected path. This algorithm takes a selected path as a target and executes sequences of operators iteratively for test cases to evolve. The evolved test case can lead the program execution to achieve the target path. A fitness function named SIMILARITY is defined to determine which test case should survive if the final test case has not been found.\",\"PeriodicalId\":403864,\"journal\":{\"name\":\"Proceedings of the Ninth Asian Test Symposium\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"55\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Ninth Asian Test Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ATS.2000.893632\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Ninth Asian Test Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATS.2000.893632","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using genetic algorithms for test case generation in path testing
Generic algorithms are inspired by Darwin's survival of the fittest theory. This paper discusses a genetic algorithm that can automatically generate test cases to test a selected path. This algorithm takes a selected path as a target and executes sequences of operators iteratively for test cases to evolve. The evolved test case can lead the program execution to achieve the target path. A fitness function named SIMILARITY is defined to determine which test case should survive if the final test case has not been found.
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