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<p>This paper presents a method and a practical implementation that complements traditional conformance testing. We infer a Mealy state machine of the system-under-test using active automata learning. This automaton is checked for bisimulation with a specification automaton modeled after the standard, which provides a strong verdict of conformance or nonconformance. We further present a method to learn models of multiple communication protocols running on the same device using a dispatcher system in conjunction with the same automata learning algorithms. We subsequently use similar checking methods to compare it with separately learned models. This allows for determining whether there is some interference or interaction between those protocols. In the practical execution of the system, we concentrate on lower levels of the Near-Field Communication (NFC, ISO/IEC 14443-3) and the Bluetooth Low-Energy (BLE) protocols. As a by-product, we share some observations of the performance of different learning algorithms and calibrations in the specific setting of ISO/IEC 14443-3, which is the difficulty to learn models of systems that a) consist of two very similar structures and b) timeout very frequently, as well as the role of conformance testing for compound models and speed optimizations for time-sensitive protocols.</p>

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Learning single and compound-protocol automata and checking behavioral equivalences

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月1日

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  • Stefan Marksteiner,
  • David Schögler,
  • Marjan Sirjani,
  • Mikael Sjödin

摘要

This paper presents a method and a practical implementation that complements traditional conformance testing. We infer a Mealy state machine of the system-under-test using active automata learning. This automaton is checked for bisimulation with a specification automaton modeled after the standard, which provides a strong verdict of conformance or nonconformance. We further present a method to learn models of multiple communication protocols running on the same device using a dispatcher system in conjunction with the same automata learning algorithms. We subsequently use similar checking methods to compare it with separately learned models. This allows for determining whether there is some interference or interaction between those protocols. In the practical execution of the system, we concentrate on lower levels of the Near-Field Communication (NFC, ISO/IEC 14443-3) and the Bluetooth Low-Energy (BLE) protocols. As a by-product, we share some observations of the performance of different learning algorithms and calibrations in the specific setting of ISO/IEC 14443-3, which is the difficulty to learn models of systems that a) consist of two very similar structures and b) timeout very frequently, as well as the role of conformance testing for compound models and speed optimizations for time-sensitive protocols.