Asynchronous message-passing systems are employed frequently to implement distributed mechanisms, protocols, and processes. This paper addresses the problem of precise data flow analysis for such systems. To obtain good precision, data flow analysis needs to somehow skip execution paths that read more messages than the number of messages sent so far in the path, as such paths are infeasible at run time. Existing data flow analysis techniques do elide a subset of such infeasible paths, but have the restriction that they admit only finite abstract analysis domains. In this paper we propose a generalization of these approaches to admit infinite abstract analysis domains, as such domains are commonly used in practice to obtain high precision. We have implemented our approach, and have analyzed its performance on a set of 14 benchmarks. On these benchmarks our tool obtains significantly higher precision compared to a baseline approach that does not elide any infeasible paths and to another baseline that elides infeasible paths but admits only finite abstract domains.

中文翻译：

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使用无限抽象域的异步系统数据流分析

异步消息传递系统经常用于实现分布式机制，协议和过程。本文解决了此类系统的精确数据流分析问题。为了获得良好的精度，数据流分析需要以某种方式跳过执行路径，该路径读取的消息比路径中到目前为止发送的消息数多，因为这种路径在运行时不可行。现有的数据流分析技术确实消除了这种不可行路径的一个子集，但存在限制，即它们仅允许使用有限的抽象分析域。在本文中，我们提出了对这些方法的概括，以允许使用无限的抽象分析域，因为这种域在实践中通常用于获得高精度。我们已经实施了我们的方法，并在一组14个基准上分析了其性能。