An extension of iStar for Machine Learning requirements by following the PRISE methodology-Reference-Cited by-同舟云学术

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An extension of iStar for Machine Learning requirements by following the PRISE methodology

Published:2024-03 Issue: Volume:88 Page:103806
ISSN:0920-5489
Container-title:Computer Standards & Interfaces
language:en
Short-container-title:Computer Standards & Interfaces

Author:

Barrera Jose M.,Reina-Reina Alejandro^ORCID,Lavalle Ana^ORCID,Maté Alejandro^ORCID,Trujillo Juan

Funder

Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana

Gobierno de España Ministerio de Ciencia e Innovación

Generalitat Valenciana

Ministerio de Ciencia e Innovación

Publisher

Elsevier BV

Subject

Law,Hardware and Architecture,Software,General Computer Science,Computer Science Applications

Reference44 articles.

1. Understanding development process of machine learning systems: Challenges and solutions;Nascimento,2019

2. Why we need biased AI – how including cognitive and ethical machine biases can enhance AI systems;Fabi,2022

3. How does machine learning change software development practices?;Wan;IEEE Trans. Softw. Eng.,2021

4. Software & Systems Requirements Engineering: In Practice;Berenbach,2009

5. A systematic literature review of istar extensions;Gonçalves;J. Syst. Softw.,2018

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