Componentwise Automata Learning for System Integration

doi:10.1007/978-3-032-08707-2_1

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Componentwise Automata Learning for System Integration

2026-01-01

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DOI：10.1007/978-3-032-08707-2_1

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Abstract

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Compositional automata learning is attracting attention as an analysis technique for complex black-box systems. It exploits a target system's internal compositional structure to reduce complexity. In this paper, we identify system integration -- the process of building a new system as a composite of potentially third-party and black-box components -- as a new application domain of compositional automata learning. Accordingly, we propose a new problem setting, where the learner has direct access to black-box components. This is in contrast with the usual problem settings of compositional learning, where the target is a legacy black-box system and queries can only be made to the whole system (but not to components). We call our problem componentwise automata learning for distinction. We identify a challenge there called component redundancies: some parts of components may not contribute to system-level behaviors, and learning them incurs unnecessary effort. We introduce a contextual componentwise learning algorithm that systematically removes such redundancies. We experimentally evaluate our proposal and show its practical relevance.

Keywords:

automata learning

compositional automata learning

systems engineering

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