A Genetic Algorithm for HMI Test Infrastructure Fine Tuning

Lukas Rosenbauer, Anthony Stein, Jörg Hähner

2021

Abstract

Human machine interfaces (HMI) have become a part of our daily lives. They are an essential part of a variety of products ranging from computers over smart phones to home appliances. Customer’s requirements for HMIs are rising and so does the complexity of the devices. Several years ago, many products had a rather simple HMI such as mere buttons. Nowadays lots of devices have screens that display complex text messages and a variety of objects such as icons. This leads to new challenges in testing, the goal of which it is to ensure quality and to find errors. We combine a genetic algorithm with computer vision techniques in order to solve two testing use cases located in the automated verification of displays. Our method has a low runtime and can be used on low budget equipment such as Raspberry Pi which reduces the operational cost in practice.

Paper Citation

in Harvard Style

Rosenbauer L., Stein A. and Hähner J. (2021). A Genetic Algorithm for HMI Test Infrastructure Fine Tuning. In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-522-7, pages 367-374. DOI: 10.5220/0010512803670374

in Bibtex Style

@conference{icinco21,
author={Lukas Rosenbauer and Anthony Stein and Jörg Hähner},
title={A Genetic Algorithm for HMI Test Infrastructure Fine Tuning},
booktitle={Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2021},
pages={367-374},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010512803670374},
isbn={978-989-758-522-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - A Genetic Algorithm for HMI Test Infrastructure Fine Tuning
SN - 978-989-758-522-7
AU - Rosenbauer L.
AU - Stein A.
AU - Hähner J.
PY - 2021
SP - 367
EP - 374
DO - 10.5220/0010512803670374