Conflict-Aware Active Automata Learning
Tiago Ferreira Léo Henry Raquel Fernandes da Silva Alexandra Silva
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.