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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.

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State Matching and Multiple References in Adaptive Active Automata Learning

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Loes Kruger,
  • Sebastian Junges,
  • Jurriaan Rot

摘要

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.