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Action Refinement in Conformance Testing

机译:一致性测试中的动作优化

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摘要

In model based testing test cases are derived from a model (the specification) of the system we want to test. In general the model is more abstract than the implementation. This may result in test cases that are not executable, because their actions are too abstract; the implementation does not understand them. The standard approach is to rewrite the model by hand to the required level of detail and regenerate the test cases. This is error-prone and time consuming. In this paper we present an approach to automatically obtain test cases at the required level of detail by means of action refinement. Action refinement is a way to add information to the abstract model. It relates actions from the abstract model to concrete actions of the system under test. We apply this approach to a simple case of action refinement, so-called atomic linear input-inputs refinement. In order to reason about correctness between an abstract model and a concrete implementation we introduce a new implementation relation. We show that this relation is equivalent with the uioco implementation relation on the refined model. Furthermore we show under which conditions the refinement of a complete abstract test suite is again complete.
机译:在基于模型的测试中,测试用例是从我们要测试的系统的模型(规范)中得出的。通常,该模型比实现更抽象。这可能导致测试用例无法执行,因为它们的动作过于抽象。实施不了解它们。标准方法是手动将模型重写为所需的详细程度,并重新生成测试用例。这是容易出错且耗时的。在本文中,我们提出了一种通过动作优化来自动获取所需详细程度的测试用例的方法。动作优化是一种向抽象模型添加信息的方法。它将抽象模型中的动作与被测系统的具体动作相关联。我们将此方法应用于动作优化的简单情况,即所谓的原子线性输入-输入优化。为了推断抽象模型和具体实现之间的正确性,我们引入了一个新的实现关系。我们表明,该关系与精炼模型上的uioco实现关系等效。此外,我们显示了在什么条件下完整的抽象测试套件的完善又一次完成。

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