Reference : An MDE Method for Improving Deep Learning Dataset Requirements Engineering using Allo...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/45161
An MDE Method for Improving Deep Learning Dataset Requirements Engineering using Alloy and UML
English
Ries, Benoit mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Guelfi, Nicolas mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Jahic, Benjamin mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Feb-2021
Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development
SCITEPRESS
41-52
Yes
No
International
978-989-758-487-9
9th International Conference on Model-Driven Engineering and Software Development
from 08-02-2021 to 10-02-2021
Online
[en] Model-Driven Engineering ; Software Engineering ; Requirements Engineering ; EMF ; Sirius ; Alloy
[en] 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.
http://hdl.handle.net/10993/45161
10.5220/0010216600410052

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