Componentwise Automata Learning for System Integration (Extended Version)

TL;DR

This paper introduces a new approach for automata learning tailored to system integration, enabling direct access to components and reducing redundant learning efforts through a novel algorithm.

Contribution

It proposes the first framework for componentwise automata learning in system integration, addressing component redundancies with a systematic removal method.

Findings

The proposed algorithm effectively reduces learning effort by removing redundancies.

Experimental results demonstrate practical applicability and efficiency.

The approach enables direct component access, improving system analysis.

Abstract

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.