Conflict-Aware Active Automata Learning (Extended Version)

TL;DR

This paper introduces C3AL, a framework that enhances active automata learning by effectively managing conflicting data, noise, and mutations, thus broadening its practical applicability.

Contribution

It proposes a conflict-aware framework that integrates observation trees with existing learners, reducing testing and improving robustness against conflicts.

Findings

C3AL outperforms traditional methods in noisy environments.

The framework handles over 30 realistic targets and 18,000 scenarios.

It reduces the number of tests needed during learning.

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.