Componentwise Automata Learning for System Integration (Extended Version)
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908480232751104 |
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| author | Fujinami, Hiroya Waga, Masaki An, Jie Suenaga, Kohei Yanagisawa, Nayuta Iseri, Hiroki Hasuo, Ichiro |
| author_facet | Fujinami, Hiroya Waga, Masaki An, Jie Suenaga, Kohei Yanagisawa, Nayuta Iseri, Hiroki Hasuo, Ichiro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04458 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Componentwise Automata Learning for System Integration (Extended Version) Fujinami, Hiroya Waga, Masaki An, Jie Suenaga, Kohei Yanagisawa, Nayuta Iseri, Hiroki Hasuo, Ichiro Formal Languages and Automata Theory 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. |
| title | Componentwise Automata Learning for System Integration (Extended Version) |
| topic | Formal Languages and Automata Theory |
| url | https://arxiv.org/abs/2508.04458 |