Conflict-Aware Active Automata Learning

TL;DR

This paper introduces C3AL, a framework that improves active automata learning by effectively managing conflicting data, noise, and system mutations, thus enhancing robustness and applicability.

Contribution

We develop a conflict-aware framework that integrates observation trees with existing learners, reducing test complexity and improving learning in noisy or mutating environments.

Findings

C3AL outperforms traditional methods in noisy scenarios

It reduces the number of tests needed during learning

Effective across diverse real-world benchmarks

Abstract

Active automata learning algorithms cannot easily handle conflict in the observation data (different outputs observed for the same inputs). This inherent inability to recover after a conflict impairs their effective applicability in scenarios where noise is present or the system under learning is mutating. We propose the Conflict-Aware Active Automata Learning (C3AL) framework to enable handling conflicting information during the learning process. The core idea is to consider the so-called observation tree as a first-class citizen in the learning process. Though this idea is explored in recent work, we take it to its full effect by enabling its use with any existing learner and minimizing the number of tests performed on the system under learning, specially in the face of conflicts. We evaluate C3AL in a large set of benchmarks, covering over 30 different realistic targets, and over 18,000 different scenarios. The results of the evaluation show that C3AL is a suitable alternative framework for closed-box learning that can better handle noise and mutations.