Learning Product Automata
This work addresses scalability in learning software systems through compositional methods, but it appears incremental as it optimizes existing active learning approaches.
The paper tackles the problem of learning Moore machines with multiple observables by decomposing them into smaller components, which reduces the number of queries needed for active learning algorithms, particularly benefiting software learning for scalability.
In this paper we give an optimization for active learning algorithms, applicable to learning Moore machines where the output comprises several observables. These machines can be decomposed themselves by projecting on each observable, resulting in smaller components. These components can then be learnt with fewer queries. This is in particular interesting for learning software, where compositional methods are important for guaranteeing scalability.