Genetic Algorithm and PSO Applied to the Choice of Hyperparameters of an MLP Neural Network for Non-Functional Requirements Classification
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/26984 |
Resumo: | Non-functional requirements (NFRs) play an important role in the Software Engineering (SE) area, being associated with the construction, operation, and maintenance of a quality application. The success of the RNF classification manual task depends on the knowledge and experience of the requirements engineer and is time-consuming. Works have been developed aiming at the application of machine learning algorithms to automatically classify RNFs, a scenario in which the hyperparameters of the classifier model must be chosen. In this work, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are used to find hyperparameters of the Multilayer Neural Perceptron Network (MLP), with the objective of classifying NFRs present in the PROMISE_exp dataset. The GA found a combination of hyperparameters that gave an F1 of 0.6349, while the PSO found a combination that got 0.6426 of F1. |
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Research, Society and Development |
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Genetic Algorithm and PSO Applied to the Choice of Hyperparameters of an MLP Neural Network for Non-Functional Requirements ClassificationAlgoritmo Genético y PSO Aplicados a la Elección de los Hiperparámetros de una Red Neuronal MLP para la Clasificación de Requisitos no FuncionalesAlgoritmo Genético e PSO Aplicados à Escolha dos Hiperparâmetros de uma Rede Neural MLP para Classificação de Requisitos Não-FuncionaisClasificaciónRequisitos de softwareAlgoritmo genéticoPSOPerceptrón multicapa.ClassificaçãoRequisitos de softwareAlgoritmo genéticoPSOPerceptron multicamadas.ClassificationSoftware requirementsGenetic AlgorithmPSOMultilayer Perceptron.Non-functional requirements (NFRs) play an important role in the Software Engineering (SE) area, being associated with the construction, operation, and maintenance of a quality application. The success of the RNF classification manual task depends on the knowledge and experience of the requirements engineer and is time-consuming. Works have been developed aiming at the application of machine learning algorithms to automatically classify RNFs, a scenario in which the hyperparameters of the classifier model must be chosen. In this work, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are used to find hyperparameters of the Multilayer Neural Perceptron Network (MLP), with the objective of classifying NFRs present in the PROMISE_exp dataset. The GA found a combination of hyperparameters that gave an F1 of 0.6349, while the PSO found a combination that got 0.6426 of F1.Los requisitos no funcionales (RNFs) juegan un papel importante en el área de Ingeniería de Software (IS), estando asociados con la construcción, operación y mantenimiento de una aplicación de calidad. El éxito de la tarea del manual de clasificación RNF depende del conocimiento y la experiencia del ingeniero de requisitos y requiere mucho tiempo. Se han desarrollado trabajos encaminados a la aplicación de algoritmos de aprendizaje automático para clasificar automáticamente los RNF, escenario en el que se deben elegir los hiperparámetros del modelo clasificador. En este trabajo, el Algoritmo Genético (GA, Genetic Algorithm) y el algoritmo de Optimización de Enjambre de Partículas (PSO, Particle Swarm Optimization) se utilizan para encontrar hiperparámetros de la Red de Perceptrón Neural Multicapa (MLP, Multilayer Perceptron), con el objetivo de clasificar los RNF presentes en el PROMISE_exp conjunto de datos. La GA encontró una combinación de hiperparámetros que dio un F1 de 0.6349, mientras que el PSO encontró una combinación que obtuvo 0.6426 de F1.Os requisitos não-funcionais (RNFs) desempenham um papel importante na área de Engenharia de Software (ES), estando associados à construção, ao funcionamento e à manutenção de uma aplicação de qualidade. O sucesso da tarefa manual de classificação de RNFs depende do conhecimento e da experiência do engenheiro de requisitos, além de demandar tempo. Trabalhos têm sido desenvolvidos visando a aplicação de algoritmos de aprendizagem de máquina para classificar automaticamente RNFs, cenário em que devem ser escolhidos os hiperparâmetros do modelo classificador. Neste trabalho, o Algoritmo Genético (GA, Genetic Algorithm) e o algoritmo de Otimização por Enxame de Partículas (PSO, Particle Swarm Optimization) são utilizados para encontrar hiperparâmetros da rede Neural Perceptron Multicamada (MLP, Multilayer Perceptron), com o objetivo de classificar RNFs presentes no conjunto de dados PROMISE_exp. O GA encontrou uma combinação de hiperparâmetros que resultou em F1 de 0,6349, enquanto o PSO encontrou uma combinação que obteve 0,6426 de F1.Research, Society and Development2022-03-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/2698410.33448/rsd-v11i3.26984Research, Society and Development; Vol. 11 No. 3; e55411326984Research, Society and Development; Vol. 11 Núm. 3; e55411326984Research, Society and Development; v. 11 n. 3; e554113269842525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/26984/23539Copyright (c) 2022 Thiago Matheus Torres Buarque; Matheus Barreto Lins Marinho; Francisco Madeiro Bernardino Juniorhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessBuarque, Thiago Matheus TorresMarinho, Matheus Barreto LinsBernardino Junior, Francisco Madeiro2022-03-09T13:44:38Zoai:ojs.pkp.sfu.ca:article/26984Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:44:51.833187Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Genetic Algorithm and PSO Applied to the Choice of Hyperparameters of an MLP Neural Network for Non-Functional Requirements Classification Algoritmo Genético y PSO Aplicados a la Elección de los Hiperparámetros de una Red Neuronal MLP para la Clasificación de Requisitos no Funcionales Algoritmo Genético e PSO Aplicados à Escolha dos Hiperparâmetros de uma Rede Neural MLP para Classificação de Requisitos Não-Funcionais |
title |
Genetic Algorithm and PSO Applied to the Choice of Hyperparameters of an MLP Neural Network for Non-Functional Requirements Classification |
spellingShingle |
Genetic Algorithm and PSO Applied to the Choice of Hyperparameters of an MLP Neural Network for Non-Functional Requirements Classification Buarque, Thiago Matheus Torres Clasificación Requisitos de software Algoritmo genético PSO Perceptrón multicapa. Classificação Requisitos de software Algoritmo genético PSO Perceptron multicamadas. Classification Software requirements Genetic Algorithm PSO Multilayer Perceptron. |
title_short |
Genetic Algorithm and PSO Applied to the Choice of Hyperparameters of an MLP Neural Network for Non-Functional Requirements Classification |
title_full |
Genetic Algorithm and PSO Applied to the Choice of Hyperparameters of an MLP Neural Network for Non-Functional Requirements Classification |
title_fullStr |
Genetic Algorithm and PSO Applied to the Choice of Hyperparameters of an MLP Neural Network for Non-Functional Requirements Classification |
title_full_unstemmed |
Genetic Algorithm and PSO Applied to the Choice of Hyperparameters of an MLP Neural Network for Non-Functional Requirements Classification |
title_sort |
Genetic Algorithm and PSO Applied to the Choice of Hyperparameters of an MLP Neural Network for Non-Functional Requirements Classification |
author |
Buarque, Thiago Matheus Torres |
author_facet |
Buarque, Thiago Matheus Torres Marinho, Matheus Barreto Lins Bernardino Junior, Francisco Madeiro |
author_role |
author |
author2 |
Marinho, Matheus Barreto Lins Bernardino Junior, Francisco Madeiro |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Buarque, Thiago Matheus Torres Marinho, Matheus Barreto Lins Bernardino Junior, Francisco Madeiro |
dc.subject.por.fl_str_mv |
Clasificación Requisitos de software Algoritmo genético PSO Perceptrón multicapa. Classificação Requisitos de software Algoritmo genético PSO Perceptron multicamadas. Classification Software requirements Genetic Algorithm PSO Multilayer Perceptron. |
topic |
Clasificación Requisitos de software Algoritmo genético PSO Perceptrón multicapa. Classificação Requisitos de software Algoritmo genético PSO Perceptron multicamadas. Classification Software requirements Genetic Algorithm PSO Multilayer Perceptron. |
description |
Non-functional requirements (NFRs) play an important role in the Software Engineering (SE) area, being associated with the construction, operation, and maintenance of a quality application. The success of the RNF classification manual task depends on the knowledge and experience of the requirements engineer and is time-consuming. Works have been developed aiming at the application of machine learning algorithms to automatically classify RNFs, a scenario in which the hyperparameters of the classifier model must be chosen. In this work, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are used to find hyperparameters of the Multilayer Neural Perceptron Network (MLP), with the objective of classifying NFRs present in the PROMISE_exp dataset. The GA found a combination of hyperparameters that gave an F1 of 0.6349, while the PSO found a combination that got 0.6426 of F1. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-03-07 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/26984 10.33448/rsd-v11i3.26984 |
url |
https://rsdjournal.org/index.php/rsd/article/view/26984 |
identifier_str_mv |
10.33448/rsd-v11i3.26984 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/26984/23539 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 11 No. 3; e55411326984 Research, Society and Development; Vol. 11 Núm. 3; e55411326984 Research, Society and Development; v. 11 n. 3; e55411326984 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
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1797052793367822336 |