State Matching and Multiple References in Adaptive Active Automata Learning

TL;DR

This paper introduces state matching and adaptive L#, a framework that leverages reference models to significantly reduce sample complexity in active automata learning, especially for multiple system variants.

Contribution

It presents a novel state matching technique and a new adaptive learning framework that utilize reference models to enhance learning efficiency.

Findings

Adaptive L# improves learning efficiency by up to two orders of magnitude.

State matching enables flexible use of reference models' structure.

Empirical results demonstrate significant performance gains over existing methods.

Abstract

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.