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Cosmic sizing of machine learning image classifier software using neural networks

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Lesterhuis, Arlan et Abran, Alain. 2019. « Cosmic sizing of machine learning image classifier software using neural networks ». In International Workshop on Software Measurement and International Conference on Software Process and Product Measurement (IWSM-Mensura) (Haarlem, The Netherlands, Oct. 07-09, 2019) Coll. « CEUR Workshop Proceedings », vol. 2476. pp. 121-129. CEUR-WS.

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Abstract

Development of machine learning software has now penetrated a large diversity of domains, in both academia and industry. From the initial realm of research with a focus on innovation and creativity, its scaling up in industry requires improved planning, monitoring and control of the development and implementation process. Such industry planning and monitoring is difficult without relevant measurement techniques adapted to the problem at hand. This paper illustrates how generic software functions can be extracted from machine learning (ML) system requirements and their functional size measured in COSMIC function points - ISO 19761. An application of these concepts is presented using an example of an ML image classifier software with a feedforward neural network

Item Type: Conference proceeding
ISBN: 16130073 (ISSN)
Editors:
Editors
ORCID
Tarhan, Ayca Kolukisa
UNSPECIFIED
Coskuncay, Ahmet
UNSPECIFIED
Professor:
Professor
Abran, Alain
Affiliation: Génie logiciel et des technologies de l'information
Date Deposited: 20 Nov 2019 21:40
Last Modified: 03 Dec 2019 16:32
URI: https://espace2.etsmtl.ca/id/eprint/19821

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