Cheng Cai, Qirun Zhang, Zhiqiang Zuo, Khanh Nguyen, G. Xu, Z. Su
{"title":"通过约束引导的cfl可达性实现调用到引用上下文的转换","authors":"Cheng Cai, Qirun Zhang, Zhiqiang Zuo, Khanh Nguyen, G. Xu, Z. Su","doi":"10.1145/3296979.3192378","DOIUrl":null,"url":null,"abstract":"A calling context is an important piece of information used widely to help developers understand program executions (e.g., for debugging). While calling contexts offer useful control information, information regarding data involved in a bug (e.g., what data structure holds a leaking object), in many cases, can bring developers closer to the bug's root cause. Such data information, often exhibited as heap reference paths, has already been needed by many tools. The only way for a dynamic analysis to record complete reference paths is to perform heap dumping, which incurs huge runtime overhead and renders the analysis impractical. This paper presents a novel static analysis that can precisely infer, from a calling context of a method that contains a use (e.g., read or write) of an object, the heap reference paths leading to the object at the time the use occurs. Since calling context recording is much less expensive, our technique provides benefits for all dynamic techniques that need heap information, significantly reducing their overhead.","PeriodicalId":50923,"journal":{"name":"ACM Sigplan Notices","volume":"74 1","pages":"196 - 210"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Calling-to-reference context translation via constraint-guided CFL-reachability\",\"authors\":\"Cheng Cai, Qirun Zhang, Zhiqiang Zuo, Khanh Nguyen, G. Xu, Z. Su\",\"doi\":\"10.1145/3296979.3192378\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A calling context is an important piece of information used widely to help developers understand program executions (e.g., for debugging). While calling contexts offer useful control information, information regarding data involved in a bug (e.g., what data structure holds a leaking object), in many cases, can bring developers closer to the bug's root cause. Such data information, often exhibited as heap reference paths, has already been needed by many tools. The only way for a dynamic analysis to record complete reference paths is to perform heap dumping, which incurs huge runtime overhead and renders the analysis impractical. This paper presents a novel static analysis that can precisely infer, from a calling context of a method that contains a use (e.g., read or write) of an object, the heap reference paths leading to the object at the time the use occurs. Since calling context recording is much less expensive, our technique provides benefits for all dynamic techniques that need heap information, significantly reducing their overhead.\",\"PeriodicalId\":50923,\"journal\":{\"name\":\"ACM Sigplan Notices\",\"volume\":\"74 1\",\"pages\":\"196 - 210\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Sigplan Notices\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3296979.3192378\",\"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/10.1145/3296979.3192378","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
Calling-to-reference context translation via constraint-guided CFL-reachability
A calling context is an important piece of information used widely to help developers understand program executions (e.g., for debugging). While calling contexts offer useful control information, information regarding data involved in a bug (e.g., what data structure holds a leaking object), in many cases, can bring developers closer to the bug's root cause. Such data information, often exhibited as heap reference paths, has already been needed by many tools. The only way for a dynamic analysis to record complete reference paths is to perform heap dumping, which incurs huge runtime overhead and renders the analysis impractical. This paper presents a novel static analysis that can precisely infer, from a calling context of a method that contains a use (e.g., read or write) of an object, the heap reference paths leading to the object at the time the use occurs. Since calling context recording is much less expensive, our technique provides benefits for all dynamic techniques that need heap information, significantly reducing their overhead.
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).