# Testing Self-Organizing Multiagent Systems

## TL;DR

This paper introduces a publish-subscribe testing approach for self-organizing multiagent systems, demonstrated through an IoT-based smart street light system, to diagnose failures in global and local behaviors.

## Contribution

It presents a novel testing methodology tailored for SASO features in MAS, addressing the gap in testing procedures for self-organizing, learning, and adaptive systems.

## Key findings

- Effective detection of failures in global behaviors
- Successful application to IoT smart street lights
- Enhanced diagnosis of local property failures

## Abstract

Multiagent Systems (MASs) involve different characteristics, such as autonomy, asynchronous and social features, which make these systems more difficult to understand. Thus, there is a lack of procedures guaranteeing that multiagent systems would behave as desired. Further complicating the situation is the fact that current agent-based approaches may also involve non-deterministic characteristics, such as learning, self-adaptation and self-organization (SASO). Nonetheless, there is a gap in the literature regarding the testing of systems with these features. This paper presents a publish-subscribe-based approach to develop test applications that facilitate the process of failure diagnosis in a self-organizing MAS. These tests are able to detect failures at the global behavior of the system or at the local properties of its parts. To illustrate the use of this approach, we developed a self-organizing MAS system based on the context of the Internet of Things (IoT), which simulates a set of smart street lights, and we performed functional ad-hoc tests. The street lights need to interact with each other in order to achieve the global goals of reducing the energy consumption and maintaining the maximum visual comfort in illuminated areas. To achieve these global behaviors, the street lights develop local behaviors automatically through a self-organizing process based on machine learning algorithms.