# State Matching and Multiple References in Adaptive Active Automata Learning

arXiv: https://arxiv.org/abs/2406.19714 · HTML: https://mlsanity.com/paper/2406.19714/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [Model-Based Testing IoT Communication via Active Automata Learning](https://mlsanity.com/paper/1904.07075/) (2019)
- [Tree-Based Adaptive Model Learning](https://mlsanity.com/paper/2209.00122/) (2022)

## Built on by (most cited first, 1 of 1)

- [Error-awareness Accelerates Active Automata Learning](https://mlsanity.com/paper/2602.21674/) (2026)

## Abstract (arXiv)

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.