An MDE Method for Improving Deep Learning Dataset Requirements Engineering using Alloy and UML

Benoít Ries, Nicolas Guelfi, Benjamin Jahić

Abstract

Since the emergence of deep learning (DL) a decade ago, only few software engineering development methods have been defined for systems based on this machine learning approach. Moreover, rare are the DL approaches addressing specifically requirements engineering. In this paper, we define a model-driven engineering (MDE) method based on traditional requirements engineering to improve datasets requirements engineering. Our MDE method is composed of a process supported by tools to aid customers and analysts in eliciting, specifying and validating dataset structural requirements for DL-based systems. Our model driven engineering approach uses the UML semi-formal modeling language for the analysis of datasets structural requirements, and the Alloy formal language for the requirements model execution based on our informal translational semantics. The model executions results are then presented to the customer for improving the dataset validation activity. Our approach aims at validating DL-based dataset structural requirements by modeling and instantiating their datatypes. We illustrate our approach with a case study on the requirements engineering of the structure of a dataset for classification of five-segments digits images.

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Paper Citation


in Harvard Style

Ries B., Guelfi N. and Jahić B. (2021). An MDE Method for Improving Deep Learning Dataset Requirements Engineering using Alloy and UML.In Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD, ISBN 978-989-758-487-9, pages 41-52. DOI: 10.5220/0010216600410052


in Bibtex Style

@conference{modelsward21,
author={Benoít Ries and Nicolas Guelfi and Benjamin Jahić},
title={An MDE Method for Improving Deep Learning Dataset Requirements Engineering using Alloy and UML},
booktitle={Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,},
year={2021},
pages={41-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010216600410052},
isbn={978-989-758-487-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,
TI - An MDE Method for Improving Deep Learning Dataset Requirements Engineering using Alloy and UML
SN - 978-989-758-487-9
AU - Ries B.
AU - Guelfi N.
AU - Jahić B.
PY - 2021
SP - 41
EP - 52
DO - 10.5220/0010216600410052