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The Ramifications of Sharing in Data Structures

机译:数据结构共享的后果

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摘要

Programs manipulating mutable data structures with intrinsic sharing present a challenge for modular verification. Deep aliasing inside data structures dramatically complicates reasoning in isolation over parts of these objects because changes to one part of the structure (say, the left child of a dag node) can affect other parts (the right child or some of its descendants) that may point into it. The result is that finding intuitive and compositional proofs of correctness is usually a struggle. We propose a compositional proof system that enables local reasoning in the presence of sharing. While the AI 'frame problem' elegantly captures the reasoning required to verify programs without sharing, we contend that natural reasoning about programs with sharing instead requires an answer to a different and more challenging AI problem, the 'ramification problem': reasoning about the indirect consequences of actions. Accordingly, we present a Ramify proof rule that attacks the ramification problem head-on and show how to reason with it. Our framework is valid in any separation logic and permits sound compositional and local reasoning in the context of both specified and unspecified sharing. We verify the correctness of a number of examples, including programs that manipulate dags, graphs, and overlaid data structures in nontrivial ways.
机译:通过内部共享操作可变数据结构的程序对模块化验证提出了挑战。数据结构内部的深度混叠极大地简化了隔离这些对象部分的推理,因为对结构的一部分(例如,dag节点的左子节点)进行更改可能会影响其他部分(右子节点或其某些后代),从而可能指向它。结果是,找到正确性的直观和组成性证明通常是一件困难的事情。我们提出了一种成分证明系统,该系统可以在存在共享的情况下进行本地推理。虽然AI的“框架问题”很好地捕捉了无需共享就可以验证程序的推理,但我们认为,共享程序的自然推理需要解决另一个更具挑战性的AI问题,即“分枝问题”:间接推理行动的后果。因此,我们提出了Ramify证明规则,该规则可正面攻击分叉问题,并说明如何推理。我们的框架在任何分离逻辑中都是有效的,并允许在指定和未指定共享的背景下进行合理的成分推理和局部推理。我们验证了许多示例的正确性,其中包括以非平凡的方式操纵dag,图和覆盖数据结构的程序。

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