{"title":"高覆盖率,无界声音预测种族检测","authors":"J. Roemer, K. Genç, Michael D. Bond","doi":"10.1145/3296979.3192385","DOIUrl":null,"url":null,"abstract":"Dynamic program analysis can predict data races knowable from an observed execution, but existing predictive analyses either miss races or cannot analyze full program executions. This paper presents Vindicator, a novel, sound (no false races) predictive approach that finds more data races than existing predictive approaches. Vindicator achieves high coverage by using a new, efficient analysis that finds all possible predictable races but may detect false races. Vindicator ensures soundness using a novel algorithm that checks each potential race to determine whether it is a true predictable race. An evaluation using large Java programs shows that Vindicator finds hard-to-detect predictable races that existing sound predictive analyses miss, at a comparable performance cost.","PeriodicalId":50923,"journal":{"name":"ACM Sigplan Notices","volume":"3 1","pages":"374 - 389"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":"{\"title\":\"High-coverage, unbounded sound predictive race detection\",\"authors\":\"J. Roemer, K. Genç, Michael D. Bond\",\"doi\":\"10.1145/3296979.3192385\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Dynamic program analysis can predict data races knowable from an observed execution, but existing predictive analyses either miss races or cannot analyze full program executions. This paper presents Vindicator, a novel, sound (no false races) predictive approach that finds more data races than existing predictive approaches. Vindicator achieves high coverage by using a new, efficient analysis that finds all possible predictable races but may detect false races. Vindicator ensures soundness using a novel algorithm that checks each potential race to determine whether it is a true predictable race. An evaluation using large Java programs shows that Vindicator finds hard-to-detect predictable races that existing sound predictive analyses miss, at a comparable performance cost.\",\"PeriodicalId\":50923,\"journal\":{\"name\":\"ACM Sigplan Notices\",\"volume\":\"3 1\",\"pages\":\"374 - 389\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"32\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Sigplan Notices\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3296979.3192385\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Sigplan Notices","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3296979.3192385","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
Dynamic program analysis can predict data races knowable from an observed execution, but existing predictive analyses either miss races or cannot analyze full program executions. This paper presents Vindicator, a novel, sound (no false races) predictive approach that finds more data races than existing predictive approaches. Vindicator achieves high coverage by using a new, efficient analysis that finds all possible predictable races but may detect false races. Vindicator ensures soundness using a novel algorithm that checks each potential race to determine whether it is a true predictable race. An evaluation using large Java programs shows that Vindicator finds hard-to-detect predictable races that existing sound predictive analyses miss, at a comparable performance cost.
期刊介绍:
The ACM Special Interest Group on Programming Languages explores programming language concepts and tools, focusing on design, implementation, practice, and theory. Its members are programming language developers, educators, implementers, researchers, theoreticians, and users. SIGPLAN sponsors several major annual conferences, including the Symposium on Principles of Programming Languages (POPL), the Symposium on Principles and Practice of Parallel Programming (PPoPP), the Conference on Programming Language Design and Implementation (PLDI), the International Conference on Functional Programming (ICFP), the International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA), as well as more than a dozen other events of either smaller size or in-cooperation with other SIGs. The monthly "ACM SIGPLAN Notices" publishes proceedings of selected sponsored events and an annual report on SIGPLAN activities. Members receive discounts on conference registrations and free access to ACM SIGPLAN publications in the ACM Digital Library. SIGPLAN recognizes significant research and service contributions of individuals with a variety of awards, supports current members through the Professional Activities Committee, and encourages future programming language enthusiasts with frequent Programming Languages Mentoring Workshops (PLMW).