Position Paper: Quality Assurance in Deep Learning Systems

Domingos Oliveira, Domingos Oliveira, Miguel Brito, Miguel Brito

2022

Abstract

The use of DL as a driving force for new and next-generation technological innovation plays a vital role in the success of organisations. Its penetration in almost all domains requires improving the quality of such systems using quality assurance models. It has been widely explored in DM and SD projects, hence the need to resort to methodology like KDD, SEMMA and the CRISP-DM. In this way, the reuse of standards and methods to guarantee the quality of these systems presents itself as an opportunity. In this way, the position paper has the fundamental objective of giving an idea about the form of a structure that facilitates the application of quality assurance in DL systems. Creating a framework that enables quality assurance of DL systems involves adjusting the development process of traditional methods since the challenge lies in the different programming paradigms and the logical representation of DL software.

Paper Citation

in Harvard Style

Oliveira D. and Brito M. (2022). Position Paper: Quality Assurance in Deep Learning Systems. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-583-8, pages 203-210. DOI: 10.5220/0011107100003269

in Bibtex Style

@conference{data22,
author={Domingos Oliveira and Miguel Brito},
title={Position Paper: Quality Assurance in Deep Learning Systems},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2022},
pages={203-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011107100003269},
isbn={978-989-758-583-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Position Paper: Quality Assurance in Deep Learning Systems
SN - 978-989-758-583-8
AU - Oliveira D.
AU - Brito M.
PY - 2022
SP - 203
EP - 210
DO - 10.5220/0011107100003269