Authors:
Domingos F. Oliveira
1
;
2
and
Miguel A. Brito
1
;
3
Affiliations:
1
Algoritmi Centre, University of Minho, Guimarães, Portugal
;
2
Department of Informatics and Computing, Mandume Ya Ndemufaio University, Lubango, Angola
;
3
Information Systems Department, University of Minho, Guimarães, Portugal
Keyword(s):
Deep Learning, Software Quality Assurance, Software Quality Assurance Standards, Quality Assurance in DL Systems.
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.