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Interval-based data refinement: A uniform approach to true concurrency in discrete and real-time systems

机译:基于间隔的数据优化:在离散和实时系统中采用统一方法实现真正​​的并发

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The majority of modern systems exhibit sophisticated concurrent behaviour, where several system components observe and modify the state with fine-grained atomicity. Many systems also exhibit truly concurrent behaviour, where multiple events may occur simultaneously. Data refinement, a correctness criterion to compare an abstract and a concrete implementation, normally admits interleaved models of execution only. In this paper, we present a method of data refinement using a framework that allows one to view a component's evolution over an interval of time, simplifying reasoning about true concurrency. By modifying the type of an interval, our theory may be specialised to cover data refinement of both discrete and real-time systems. We develop a sound interval-based forward simulation rule that enables decomposition of data refinement proofs, and apply this rule to verify data refinement for two examples: a simple concurrent program and a more in-depth real-time controller.
机译:大多数现代系统都表现出复杂的并发行为,其中几个系统组件以细粒度的原子性观察和修改状态。许多系统还表现出真正的并发行为,其中可能同时发生多个事件。数据细化是比较抽象和具体实现的正确性标准,通常只接受交错执行模型。在本文中,我们提出了一种使用框架的数据优化方法,该框架允许人们在一定时间间隔内查看组件的演变,从而简化了有关真正并发性的推理。通过修改间隔的类型,我们的理论可能会专门涵盖离散系统和实时系统的数据优化。我们开发了一个基于声音间隔的正向仿真规则,该规则可以分解数据优化证明,并针对两个示例应用此规则来验证数据优化:一个简单的并发程序和一个更深入的实时控制器。

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