# Monitoring Informed Testing for IoT

## TL;DR

This paper presents a framework for monitoring and testing IoT systems by leveraging data-driven usage profiles to improve test prioritization, system analysis, and runtime monitoring across distributed VM environments.

## Contribution

It introduces a novel framework for adaptive IoT testing that utilizes usage profiles and discusses VM allocation challenges for scalable testing.

## Key findings

- Usage profiles enhance test prioritization.
- Predictive analysis identifies error-prone system locations.
- Framework supports layered testing in distributed VM environments.

## Abstract

Internet of Things (IoT) systems continuously collect a large amount of data from heterogeneous "smart objects" through standardised service interfaces. A key challenge is how to use these data and relevant event logs to construct continuously adapted usage profiles and apply them to enhance testing methods, i.e., prioritization of tests for the testing of continuous integration of an IoT system. In addition, these usage profiles provide relevance weightings to analyse architecture and behaviour of the system. Based on the analysis, testing methods can predict specific system locations that are susceptible to error, and therefore suggest where expanded runtime monitoring is necessary. Furthermore, IoT aims to connect billions of "smart devices" over the network. Testing even a small IoT system connecting a few dozens of smart devices would require a network of test Virtual Machines (VMs) possibly spreading across the Fog and the Cloud. In this paper we propose a framework for testing of each IoT layer in a separate VM environment, and discuss potential difficulties with optimal VM allocation.