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Unifying modal interface theories and compositional input/output conformance testing

机译:统一模态接口理论和成分输入/输出一致性测试

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We present a novel formal foundation for model-based testing of component-based software systems, unifying principles from modal interface theories and input/output conformance testing. Our theory relies on Modal Interface Automata with Input Refusals (IR-MIA) as behavioral formalism for both the specification and the implementation under test. IR-MIA models allow for a fine-grained distinction between mandatory and optional, as well as between underspecified and forbidden input/output behaviors, thus improving expressiveness of existing approaches in various ways. The input/output conformance relation on IR-MIA, called modal-irioco, therefore supports positive and negative conformance testing with optimistic and pessimistic environmental assumptions and is preserved under modal refinement. Our theory further adapts a variety of different composition operators from interfaces theories to IR-MIA models, each enjoying-under moderate restrictions-desirable compositionality properties with respect to the modalirioco relation. Those operators include interface conjunction for incrementally integrating multiple perspectives on the same component, as well as parallel composition with multicast and hiding for integrating multiple concurrently interacting components. Furthermore, a quotient operator on IR-MIA, serving as the inverse to parallel composition, facilitates decomposition of conformance testing, thus providing a solution to the unknown-component problem. (C) 2018 Elsevier B.V. All rights reserved.
机译:我们为基于组件的软件系统的基于模型的测试,基于模态接口理论和输入/输出一致性测试的统一原则提供了新颖的正式基础。我们的理论依赖于带有输入拒绝的模态接口自动机(IR-MIA),作为规范和被测实现的行为形式。 IR-MIA模型可以对强制性和可选性以及未指定和禁止的输入/输出行为之间的细微区别,从而以各种方式提高了现有方法的表现力。因此,IR-MIA上的输入/输出一致性关系称为modal-irioco,它根据乐观和悲观的环境假设支持正负一致性测试,并在模态优化下得以保留。从界面理论到IR-MIA模型,我们的理论进一步适应了各种不同的合成算子,每个算子在适度的限制下都具有相对于modalirioco关系的理想合成性质。这些运算符包括用于逐步集成同一组件上的多个视角的接口连接,以及用于多播和隐藏的并行组合以集成多个并发交互组件。此外,IR-MIA上的商算子用作并行组成的逆函数,有助于进行一致性测试的分解,从而为未知成分问题提供了解决方案。 (C)2018 Elsevier B.V.保留所有权利。

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