{"title":"SMT的动态算法选择","authors":"Nikhil Pimpalkhare","doi":"10.1145/3324884.3418922","DOIUrl":null,"url":null,"abstract":"We describe an online approach to SMT solver selection using nearest neighbor classification and runtime estimation. We implement and evaluate our approach with MedleySolver, finding that it makes nearly optimal selections and evaluates a dataset of queries three times faster than any indivdual solver.","PeriodicalId":106337,"journal":{"name":"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic Algorithm Selection for SMT\",\"authors\":\"Nikhil Pimpalkhare\",\"doi\":\"10.1145/3324884.3418922\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe an online approach to SMT solver selection using nearest neighbor classification and runtime estimation. We implement and evaluate our approach with MedleySolver, finding that it makes nearly optimal selections and evaluates a dataset of queries three times faster than any indivdual solver.\",\"PeriodicalId\":106337,\"journal\":{\"name\":\"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3324884.3418922\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3324884.3418922","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We describe an online approach to SMT solver selection using nearest neighbor classification and runtime estimation. We implement and evaluate our approach with MedleySolver, finding that it makes nearly optimal selections and evaluates a dataset of queries three times faster than any indivdual solver.
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