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Discovery of invariants through automated theory formation

机译:通过自动理论形成发现不变量

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摘要

Refinement is a powerful mechanism for mastering the complexities that arise when formally modelling systems. Refinement also brings with it additional proof obligations -- requiring a developer to discover properties relating to their design decisions. With the goal of reducing this burden, we have investigated how a general purpose automated theory formation tool, HR, can be used to automate the discovery of such properties within the context of the Event-B formal modelling framework. This gave rise to an integrated approach to automated invariant discovery. In addition to formal modelling and automated theory formation, our approach relies upon the simulation of system models as a key input to the invariant discovery process. Moreover we have developed a set of heuristics which, when coupled with automated proof-failure analysis, have enabled us to effectively tailor HR to the needs of Event-B developments. Drawing in part upon case study material from the literature, we have achieved some promising experimental results. While our focus has been on Event-B, we believe that our approach could be applied more widely to formal modelling frameworks which support simulation.
机译:精炼是一种强大的机制,可用于掌握在对系统进行正式建模时出现的复杂性。精炼还带来了额外的证明义务-要求开发人员发现与他们的设计决策有关的属性。为了减轻这种负担,我们研究了如何在Event-B正式建模框架的背景下使用通用的自动化理论形成工具HR自动发现此类属性。这产生了用于自动不变发现的集成方法。除了形式化建模和自动理论形成之外,我们的方法还依赖于系统模型的仿真,作为对不变式发现过程的关键输入。此外,我们已经开发了一套启发式方法,结合自动的失败证明分析,使我们能够有效地根据Event-B开发的需求定制人力资源。部分利用文献中的案例研究材料,我们已经获得了一些有希望的实验结果。尽管我们的重点一直放在事件B上,但我们认为我们的方法可以更广泛地应用于支持模拟的正式建模框架。

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