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.

翻译：主动自动机学习（AAL）是一种通过与黑盒系统交互来推断状态机的方法。自适应AAL旨在通过以（相似）参考模型的形式融入领域特定知识，以降低AAL的样本复杂度。此类参考模型在学习软件系统的多个版本或变体时自然出现。本文提出状态匹配方法，使学习器能够灵活利用这些参考模型的结构。状态匹配是自适应L#的主要组成部分，后者是基于L#构建的新型自适应学习框架。我们的实证评估表明，自适应L#将现有技术水平提升了高达两个数量级。