# Model-Based Testing IoT Communication via Active Automata Learning

## TL;DR

This paper introduces a semi-automatic, learning-based method for detecting failures in IoT communication systems by inferring and cross-checking models of multiple implementations, demonstrated through MQTT broker case studies.

## Contribution

It presents a novel approach combining active automata learning and cross-implementation comparison for failure detection in IoT protocols without prior detailed models.

## Key findings

- Most MQTT implementations tested exhibited violations of the protocol.
- The approach successfully identified faulty behaviors in multiple implementations.
- The method is effective for semi-automatic failure detection in reactive IoT systems.

## Abstract

This paper presents a learning-based approach to detecting failures in reactive systems. The technique is based on inferring models of multiple implementations of a common specification which are pair-wise cross-checked for equivalence. Any counterexample to equivalence is flagged as suspicious and has to be analysed manually. Hence, it is possible to find possible failures in a semi-automatic way without prior modelling. We show that the approach is effective by means of a case study. For this case study, we carried out experiments in which we learned models of five implementations of MQTT brokers/servers, a protocol used in the Internet of Things. Examining these models, we found several violations of the MQTT specification. All but one of the considered implementations showed faulty behaviour. In the analysis, we discuss effectiveness and also issues we faced.