Hybrid neural network approach for predicting maintainability of object-oriented software
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | INFOCOMP: Jornal de Ciência da Computação |
Texto Completo: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/391 |
Resumo: | Estimation of different parameters for object-oriented systems development such as effort, quality, and risk is of major concern in software development life cycle. Majority of the approaches available in literature for estimation are based on regression analysis and neural network techniques. Also it is observed that numerous software metrics are being used as input for estimation. In this study, object-oriented metrics have been considered to provide requisite input data to design the models for prediction of maintainability using three artificial intelligence (AI) techniques such as neural network, Neuro-Genetic (hybrid approach of neural network and genetic algorithm) and Neuro-PSO (hybrid approach of neural network and Particle Swarm Optimization). These three AI techniques are applied to predict maintainability on two case studies such as User Interface System (UIMS) and Quality Evaluation System (QUES). The performance of all three AI techniques were evaluated based on the various parameters available in literature such as mean absolute error (MAE) and mean Absolute Relative Error (MARE). Experimental results show that the hybrid technique utilizing Neuro-PSO technique achieved better result for prediction of maintainability when compared with the other two. |
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INFOCOMP: Jornal de Ciência da Computação |
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Hybrid neural network approach for predicting maintainability of object-oriented softwareArtificial neural networksoftware metricsGenetic algorithmmaintainabilityNeuronsParticle swarm optimizationQUESUIMS.Estimation of different parameters for object-oriented systems development such as effort, quality, and risk is of major concern in software development life cycle. Majority of the approaches available in literature for estimation are based on regression analysis and neural network techniques. Also it is observed that numerous software metrics are being used as input for estimation. In this study, object-oriented metrics have been considered to provide requisite input data to design the models for prediction of maintainability using three artificial intelligence (AI) techniques such as neural network, Neuro-Genetic (hybrid approach of neural network and genetic algorithm) and Neuro-PSO (hybrid approach of neural network and Particle Swarm Optimization). These three AI techniques are applied to predict maintainability on two case studies such as User Interface System (UIMS) and Quality Evaluation System (QUES). The performance of all three AI techniques were evaluated based on the various parameters available in literature such as mean absolute error (MAE) and mean Absolute Relative Error (MARE). Experimental results show that the hybrid technique utilizing Neuro-PSO technique achieved better result for prediction of maintainability when compared with the other two.Editora da UFLA2014-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/391INFOCOMP Journal of Computer Science; Vol. 13 No. 2 (2014): December, 2014; 10-211982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/391/371Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessKumar, LovRath, Santanu Ku.2015-08-06T13:12:00Zoai:infocomp.dcc.ufla.br:article/391Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:35.372469INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
Hybrid neural network approach for predicting maintainability of object-oriented software |
title |
Hybrid neural network approach for predicting maintainability of object-oriented software |
spellingShingle |
Hybrid neural network approach for predicting maintainability of object-oriented software Kumar, Lov Artificial neural network software metrics Genetic algorithm maintainability Neurons Particle swarm optimization QUES UIMS. |
title_short |
Hybrid neural network approach for predicting maintainability of object-oriented software |
title_full |
Hybrid neural network approach for predicting maintainability of object-oriented software |
title_fullStr |
Hybrid neural network approach for predicting maintainability of object-oriented software |
title_full_unstemmed |
Hybrid neural network approach for predicting maintainability of object-oriented software |
title_sort |
Hybrid neural network approach for predicting maintainability of object-oriented software |
author |
Kumar, Lov |
author_facet |
Kumar, Lov Rath, Santanu Ku. |
author_role |
author |
author2 |
Rath, Santanu Ku. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Kumar, Lov Rath, Santanu Ku. |
dc.subject.por.fl_str_mv |
Artificial neural network software metrics Genetic algorithm maintainability Neurons Particle swarm optimization QUES UIMS. |
topic |
Artificial neural network software metrics Genetic algorithm maintainability Neurons Particle swarm optimization QUES UIMS. |
description |
Estimation of different parameters for object-oriented systems development such as effort, quality, and risk is of major concern in software development life cycle. Majority of the approaches available in literature for estimation are based on regression analysis and neural network techniques. Also it is observed that numerous software metrics are being used as input for estimation. In this study, object-oriented metrics have been considered to provide requisite input data to design the models for prediction of maintainability using three artificial intelligence (AI) techniques such as neural network, Neuro-Genetic (hybrid approach of neural network and genetic algorithm) and Neuro-PSO (hybrid approach of neural network and Particle Swarm Optimization). These three AI techniques are applied to predict maintainability on two case studies such as User Interface System (UIMS) and Quality Evaluation System (QUES). The performance of all three AI techniques were evaluated based on the various parameters available in literature such as mean absolute error (MAE) and mean Absolute Relative Error (MARE). Experimental results show that the hybrid technique utilizing Neuro-PSO technique achieved better result for prediction of maintainability when compared with the other two. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-12-01 |
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://infocomp.dcc.ufla.br/index.php/infocomp/article/view/391 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/391 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/391/371 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da UFLA |
publisher.none.fl_str_mv |
Editora da UFLA |
dc.source.none.fl_str_mv |
INFOCOMP Journal of Computer Science; Vol. 13 No. 2 (2014): December, 2014; 10-21 1982-3363 1807-4545 reponame:INFOCOMP: Jornal de Ciência da Computação instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
INFOCOMP: Jornal de Ciência da Computação |
collection |
INFOCOMP: Jornal de Ciência da Computação |
repository.name.fl_str_mv |
INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
infocomp@dcc.ufla.br||apfreire@dcc.ufla.br |
_version_ |
1799874741438251008 |