Performance indicators analysis in software processes using semi-supervised learning with information visualization

Detalhes bibliográficos
Autor(a) principal: Bodo, Leandro [UNESP]
Data de Publicação: 2016
Outros Autores: de Oliveira, Hilda Carvalho [UNESP], Breve, Fabricio Aparecido [UNESP], Eler, Danilo Medeiros [UNESP]
Tipo de documento: Capítulo de livro
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-319-32467-8_49
http://hdl.handle.net/11449/177936
Resumo: Software development process requires judicious quality control, using performance indicators to support decision-making in the different processes chains. This paper recommends the use of machine learning with the semi supervised algorithms to analyze these indicators. In this context, this paper proposes the use of visualization techniques of multidimensional information to support the labeling process of samples, increasing the reliability of the labeled indicators (group or individual). The experiments show analysis from real indicators data of a software development company and use the algorithm bioinspired Particle Competition and Cooperation. The information visualization techniques used are: Least Square Projection, Classical Multidimensional Scaling and Parallel Coordinates. Those techniques help to correct the labeling process performed by specialists (labelers), enabling the identification of mistakes in order to improve the data accuracy for application of the semi-supervised algorithm.
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spelling Performance indicators analysis in software processes using semi-supervised learning with information visualizationBSCInformation visualizationMachine learningMPS-SWPerformance indicatorsSoftware processesSoftware qualitySoftware development process requires judicious quality control, using performance indicators to support decision-making in the different processes chains. This paper recommends the use of machine learning with the semi supervised algorithms to analyze these indicators. In this context, this paper proposes the use of visualization techniques of multidimensional information to support the labeling process of samples, increasing the reliability of the labeled indicators (group or individual). The experiments show analysis from real indicators data of a software development company and use the algorithm bioinspired Particle Competition and Cooperation. The information visualization techniques used are: Least Square Projection, Classical Multidimensional Scaling and Parallel Coordinates. Those techniques help to correct the labeling process performed by specialists (labelers), enabling the identification of mistakes in order to improve the data accuracy for application of the semi-supervised algorithm.Department of Statistics Applied Mathematics and Computer Science Unesp - Universidade Estadual PaulistaDepartment of Mathematics and Computer Science Unesp - Universidade Estadual PaulistaUnesp - Universidade Estadual PaulistaDepartment of Statistics Applied Mathematics and Computer Science Unesp - Universidade Estadual PaulistaDepartment of Mathematics and Computer Science Unesp - Universidade Estadual PaulistaUnesp - Universidade Estadual PaulistaUniversidade Estadual Paulista (Unesp)Bodo, Leandro [UNESP]de Oliveira, Hilda Carvalho [UNESP]Breve, Fabricio Aparecido [UNESP]Eler, Danilo Medeiros [UNESP]2018-12-11T17:27:47Z2018-12-11T17:27:47Z2016-04-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookPart555-568http://dx.doi.org/10.1007/978-3-319-32467-8_49Advances in Intelligent Systems and Computing, v. 448, p. 555-568.2194-5357http://hdl.handle.net/11449/17793610.1007/978-3-319-32467-8_492-s2.0-8496262619256938600255383270000-0002-1123-9784Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAdvances in Intelligent Systems and Computinginfo:eu-repo/semantics/openAccess2024-06-19T14:32:16Zoai:repositorio.unesp.br:11449/177936Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-06-19T14:32:16Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Performance indicators analysis in software processes using semi-supervised learning with information visualization
title Performance indicators analysis in software processes using semi-supervised learning with information visualization
spellingShingle Performance indicators analysis in software processes using semi-supervised learning with information visualization
Bodo, Leandro [UNESP]
BSC
Information visualization
Machine learning
MPS-SW
Performance indicators
Software processes
Software quality
title_short Performance indicators analysis in software processes using semi-supervised learning with information visualization
title_full Performance indicators analysis in software processes using semi-supervised learning with information visualization
title_fullStr Performance indicators analysis in software processes using semi-supervised learning with information visualization
title_full_unstemmed Performance indicators analysis in software processes using semi-supervised learning with information visualization
title_sort Performance indicators analysis in software processes using semi-supervised learning with information visualization
author Bodo, Leandro [UNESP]
author_facet Bodo, Leandro [UNESP]
de Oliveira, Hilda Carvalho [UNESP]
Breve, Fabricio Aparecido [UNESP]
Eler, Danilo Medeiros [UNESP]
author_role author
author2 de Oliveira, Hilda Carvalho [UNESP]
Breve, Fabricio Aparecido [UNESP]
Eler, Danilo Medeiros [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Bodo, Leandro [UNESP]
de Oliveira, Hilda Carvalho [UNESP]
Breve, Fabricio Aparecido [UNESP]
Eler, Danilo Medeiros [UNESP]
dc.subject.por.fl_str_mv BSC
Information visualization
Machine learning
MPS-SW
Performance indicators
Software processes
Software quality
topic BSC
Information visualization
Machine learning
MPS-SW
Performance indicators
Software processes
Software quality
description Software development process requires judicious quality control, using performance indicators to support decision-making in the different processes chains. This paper recommends the use of machine learning with the semi supervised algorithms to analyze these indicators. In this context, this paper proposes the use of visualization techniques of multidimensional information to support the labeling process of samples, increasing the reliability of the labeled indicators (group or individual). The experiments show analysis from real indicators data of a software development company and use the algorithm bioinspired Particle Competition and Cooperation. The information visualization techniques used are: Least Square Projection, Classical Multidimensional Scaling and Parallel Coordinates. Those techniques help to correct the labeling process performed by specialists (labelers), enabling the identification of mistakes in order to improve the data accuracy for application of the semi-supervised algorithm.
publishDate 2016
dc.date.none.fl_str_mv 2016-04-01
2018-12-11T17:27:47Z
2018-12-11T17:27:47Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1007/978-3-319-32467-8_49
Advances in Intelligent Systems and Computing, v. 448, p. 555-568.
2194-5357
http://hdl.handle.net/11449/177936
10.1007/978-3-319-32467-8_49
2-s2.0-84962626192
5693860025538327
0000-0002-1123-9784
url http://dx.doi.org/10.1007/978-3-319-32467-8_49
http://hdl.handle.net/11449/177936
identifier_str_mv Advances in Intelligent Systems and Computing, v. 448, p. 555-568.
2194-5357
10.1007/978-3-319-32467-8_49
2-s2.0-84962626192
5693860025538327
0000-0002-1123-9784
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Advances in Intelligent Systems and Computing
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 555-568
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1803045448471019520

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