State Matching and Multiple References in Adaptive Active Automata Learning
This addresses the efficiency of learning state machines for software systems, particularly when multiple versions or variants exist, representing a strong specific gain.
The paper tackled the problem of reducing sample complexity in active automata learning by introducing state matching to flexibly incorporate reference models, resulting in adaptive L# which improved the state of the art by up to two orders of magnitude.
Active automata learning (AAL) is a method to infer state machines by interacting with black-box systems. Adaptive AAL aims to reduce the sample complexity of AAL by incorporating domain specific knowledge in the form of (similar) reference models. Such reference models appear naturally when learning multiple versions or variants of a software system. In this paper, we present state matching, which allows flexible use of the structure of these reference models by the learner. State matching is the main ingredient of adaptive L#, a novel framework for adaptive learning, built on top of L#. Our empirical evaluation shows that adaptive L# improves the state of the art by up to two orders of magnitude.