{"title":"具有高阶多态类型和高阶效应算子的多变流分析","authors":"Stefan Holdermans, Jurriaan Hage","doi":"10.1145/1863543.1863554","DOIUrl":null,"url":null,"abstract":"We present a type and effect system for flow analysis that makes essential use of higher-ranked polymorphism. We show that, for higher-order functions, the expressiveness of higher-ranked types enables us to improve on the precision of conventional let-polymorphic analyses. Modularity and decidability of the analysis are guaranteed by making the analysis of each program parametric in the analyses of its inputs; in particular, we have that higher-order functions give rise to higher-order operations on effects. As flow typing is archetypical to a whole class of type and effect systems, our approach can be used to boost the precision of a wide range of type-based program analyses for higher-order languages.","PeriodicalId":20504,"journal":{"name":"Proceedings of the 18th ACM SIGPLAN international conference on Functional programming","volume":"43 1","pages":"63-74"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Polyvariant flow analysis with higher-ranked polymorphic types and higher-order effect operators\",\"authors\":\"Stefan Holdermans, Jurriaan Hage\",\"doi\":\"10.1145/1863543.1863554\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a type and effect system for flow analysis that makes essential use of higher-ranked polymorphism. We show that, for higher-order functions, the expressiveness of higher-ranked types enables us to improve on the precision of conventional let-polymorphic analyses. Modularity and decidability of the analysis are guaranteed by making the analysis of each program parametric in the analyses of its inputs; in particular, we have that higher-order functions give rise to higher-order operations on effects. As flow typing is archetypical to a whole class of type and effect systems, our approach can be used to boost the precision of a wide range of type-based program analyses for higher-order languages.\",\"PeriodicalId\":20504,\"journal\":{\"name\":\"Proceedings of the 18th ACM SIGPLAN international conference on Functional programming\",\"volume\":\"43 1\",\"pages\":\"63-74\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 18th ACM SIGPLAN international conference on Functional programming\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1863543.1863554\",\"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 18th ACM SIGPLAN international conference on Functional programming","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1863543.1863554","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Polyvariant flow analysis with higher-ranked polymorphic types and higher-order effect operators
We present a type and effect system for flow analysis that makes essential use of higher-ranked polymorphism. We show that, for higher-order functions, the expressiveness of higher-ranked types enables us to improve on the precision of conventional let-polymorphic analyses. Modularity and decidability of the analysis are guaranteed by making the analysis of each program parametric in the analyses of its inputs; in particular, we have that higher-order functions give rise to higher-order operations on effects. As flow typing is archetypical to a whole class of type and effect systems, our approach can be used to boost the precision of a wide range of type-based program analyses for higher-order languages.