Testing Compositionality（组合性测试）
Testing Compositionality
AI总结：
该研究针对基于模型的测试中的组合性问题，提出三种应用互接受性的算法，支持场景适配与效率权衡，实现大型系统测试的组件化分解。
摘要：
组合性支持通过对组件的操作来处理大型系统。对于基于模型的测试而言，这意味着可以通过对组件进行建模和测试来测试大型系统：所有组件通过测试即意味着整个系统通过测试。在先前的工作中，我们为规格模型定义了互接受性（mutual acceptance），并证明该属性是基于模型的测试中组合性的充分条件。\n本文提出了三种在实践中应用互接受性的核心算法。其一，可在规格上验证互接受性，从而证明所有有效实现的组合性。其二，我们提出了一种可靠且穷尽的基于模型的测试流程，用于在特定黑盒实现上检查互接受性。其结果是，大型系统的正确性测试可分解为：测试组件实现是否符合其规格的uioco一致性，以及测试环境是否符合其环境规格的环境一致性。其三，我们通过同时利用多个规格施加的约束进一步优化了该流程。这三种算法共同支持针对给定场景选择最合适的方法，可根据需求通过针对特定上下文优化，以更具普适性的结果换取更快的运行速度。
英文摘要：
Compositionality supports the manipulation of large systems by working on their components. For model-based testing, this means that large systems can be tested by modelling and testing their components: passing tests for all components implies passing tests for the whole system. In previous work, we defined mutual acceptance for specification models and proved that this property is a sufficient condition for compositionality in model-based testing. In this paper, we present three main algorithms for using mutual acceptance in practice. First, we can verify mutual acceptance on specifications, proving compositionality for all valid implementations. Second, we give a sound and exhaustive model-based testing procedure which checks mutual acceptance on a specific black-box implementation. The result is that testing the correctness of large systems can be decomposed into testing the component implementations for uioco conformance to their specifications, and testing for environmental conformance to the specifications of their environment. Finally, we optimise this procedure further by utilizing the constraints imposed by multiple specifications at the same time. These three algorithms together allow picking the most suitable approach for a given situation, trading in more generalizable results for faster runtime by optimising for a specific context as desired.
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