An Analysis and Simulation Framework for Systems with Classification Components

Francesco Bedini, Tino Jungebloud, Ralph Maschotta, Armin Zimmermann

2024

Abstract

Machine learning solutions are becoming more widespread as they can solve some classes of problems better than traditional software. Hence, industries look forward to integrating this new technology into their products and workflows. However, this calls for new models and analysis concepts in systems design that can incorporate the properties and effects of machine learning components. In this paper, we propose a framework that allows designing, analyzing, and simulating hardware-software systems that contain deep learning classification components. We focus on the modeling and predicting uncertainty aspects, which are typical for machine-learning applications. They may lead to incorrect results that may negatively affect the entire system’s dependability, reliability, and even safety. This issue is receiving increasing attention as “explain-able” or “certifiable” AI. We propose a Domain-Specific Language with a precise stochastic colored Petri net semantics to model such systems, which then can be simulated and analyzed to compute performance and reliability measures. The language is extensible and allows adding parameters to any of its elements, supporting the definition of additional analysis methods for future modular extensions.

Paper Citation

in Harvard Style

Bedini F., Jungebloud T., Maschotta R. and Zimmermann A. (2024). An Analysis and Simulation Framework for Systems with Classification Components. In Proceedings of the 12th International Conference on Model-Based Software and Systems Engineering - Volume 1: MODELSWARD; ISBN 978-989-758-682-8, SciTePress, pages 50-61. DOI: 10.5220/0012357000003645

in Bibtex Style

@conference{modelsward24,
author={Francesco Bedini and Tino Jungebloud and Ralph Maschotta and Armin Zimmermann},
title={An Analysis and Simulation Framework for Systems with Classification Components},
booktitle={Proceedings of the 12th International Conference on Model-Based Software and Systems Engineering - Volume 1: MODELSWARD},
year={2024},
pages={50-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012357000003645},
isbn={978-989-758-682-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Model-Based Software and Systems Engineering - Volume 1: MODELSWARD
TI - An Analysis and Simulation Framework for Systems with Classification Components
SN - 978-989-758-682-8
AU - Bedini F.
AU - Jungebloud T.
AU - Maschotta R.
AU - Zimmermann A.
PY - 2024
SP - 50
EP - 61
DO - 10.5220/0012357000003645
PB - SciTePress