  A bottleneck in modern active automata learning is to test whether a
hypothesized Mealy machine correctly describes the system under learning. The
search space for possible counterexamples is given by so-called test suites,
consisting of input sequences that have to be checked to decide whether a
counterexample exists. This paper shows that significantly smaller test suites
suffice under reasonable assumptions on the structure of the black box. These
smaller test suites help to refute false hypotheses during active automata
learning, even when the assumptions do not hold. We combine multiple test
suites using a multi-armed bandit setup that adaptively selects a test suite.
An extensive empirical evaluation shows the efficacy of our approach. For small
to medium-sized models, the performance gain is limited. However, the approach
allows learning models from large, industrial case studies that were beyond the
reach of known methods.
