State Matching and Multiple References in Adaptive Active Automata Learning

TL;DR

This work tackles the high sample complexity of active automata learning by introducing state matching, a flexible mechanism to reuse informative parts of one or more reference automata. It integrates state matching with an extended L# framework, AL#, that also incorporates rebuilding from references and supports multiple references, to efficiently learn Mealy machines from evolving or variant systems. The authors provide termination and complexity analyses and demonstrate up to two orders of magnitude reductions in input queries across diverse benchmarks, including OpenSSL and DTLS/TCP suites. The approach advances adaptive AAL by enabling robust reuse of prior knowledge and multiple references, with practical implications for scalable protocol inference and software evolution analysis.

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.

Figures (5)

• Figure 1: An SUL $\mathcal{S}$ and three reference models $\mathcal{R}_1$, $\mathcal{R}_2$ and $\mathcal{R}_3$.
• Figure 2: $\mathcal{T}$
• Figure 3: $\mathcal{H}$
• Figure 4: $\mathcal{T}'$
• Figure 5: $\mathcal{H}'$

Theorems & Definitions (4)

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