🤖 AI Summary
This paper addresses the compositional verification challenge in model-driven testing: whether component-level testing guarantees system-level correctness. We propose a novel paradigm grounded in mutual acceptance and environment consistency. First, we formally define environment consistency and apply it to black-box compositional testing. Second, we design the first decidable mutual acceptance verification algorithm, accompanied by a sound and complete testing procedure. Third, we establish a model-driven theoretical framework that enables rigorous derivation of system-wide correctness from component-level test outcomes. Our approach preserves testing completeness while substantially reducing the complexity of verifying large-scale systems. By bridging formal semantics with practical testability, it provides a feasible, verifiable pathway for high-assurance system validation. (124 words)
📝 Abstract
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 [13], we defined mutual acceptance for specification models and proved that this is a sufficient condition for compositionality in model-based testing. In this paper, we present an algorithm for verifying mutual acceptance on specifications and a sound and exhaustive model-based test procedure for checking mutual acceptance on black-box implementations, both inspired by the idea of environmental conformance [7,8]. The result is that correctness of large systems can be determined by testing the component implementations for conformance to their component specification and for environmental conformance to the specification of their environment.