{"title":"基于测试的程序修复中适应度函数的改进","authors":"J. Petke, Aymeric Blot","doi":"10.1145/3387940.3392180","DOIUrl":null,"url":null,"abstract":"Genetic improvement has proved to be a successful technique in optimising various software properties, such as bug fixing, runtime improvement etc. It uses automated search to find improved program variants. Usually the evaluation of each mutated program involves running a test suite, and then calculating the fitness based on Boolean test case results. This, however, creates plateaus in the fitness landscape that are hard for search to efficiently traverse. Therefore, we propose to consider a more fine-grained fitness function that takes the output of test case assertions into account.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Refining Fitness Functions in Test-Based Program Repair\",\"authors\":\"J. Petke, Aymeric Blot\",\"doi\":\"10.1145/3387940.3392180\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Genetic improvement has proved to be a successful technique in optimising various software properties, such as bug fixing, runtime improvement etc. It uses automated search to find improved program variants. Usually the evaluation of each mutated program involves running a test suite, and then calculating the fitness based on Boolean test case results. This, however, creates plateaus in the fitness landscape that are hard for search to efficiently traverse. Therefore, we propose to consider a more fine-grained fitness function that takes the output of test case assertions into account.\",\"PeriodicalId\":309659,\"journal\":{\"name\":\"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3387940.3392180\",\"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 IEEE/ACM 42nd International Conference on Software Engineering Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3387940.3392180","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Refining Fitness Functions in Test-Based Program Repair
Genetic improvement has proved to be a successful technique in optimising various software properties, such as bug fixing, runtime improvement etc. It uses automated search to find improved program variants. Usually the evaluation of each mutated program involves running a test suite, and then calculating the fitness based on Boolean test case results. This, however, creates plateaus in the fitness landscape that are hard for search to efficiently traverse. Therefore, we propose to consider a more fine-grained fitness function that takes the output of test case assertions into account.
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