IDS: An Incremental Learning Algorithm for Finite Automata

TL;DR

The paper introduces IDS, an efficient incremental learning algorithm for deterministic finite automata, with proven correctness and empirical analysis demonstrating its suitability for software engineering tasks like testing and model inference.

Contribution

It presents a novel incremental learning algorithm for DFA based on distinguishing sequences, with rigorous correctness proof and empirical performance evaluation.

Findings

IDS is effective in learning DFA in the limit

The algorithm performs well in terms of learning times

Suitable for software testing and model inference applications

Abstract

We present a new algorithm IDS for incremental learning of deterministic finite automata (DFA). This algorithm is based on the concept of distinguishing sequences introduced in (Angluin81). We give a rigorous proof that two versions of this learning algorithm correctly learn in the limit. Finally we present an empirical performance analysis that compares these two algorithms, focussing on learning times and different types of learning queries. We conclude that IDS is an efficient algorithm for software engineering applications of automata learning, such as testing and model inference.