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

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dc.contributor.authorBodo, Leandro [UNESP]
dc.contributor.authorde Oliveira, Hilda Carvalho [UNESP]
dc.contributor.authorBreve, Fabricio Aparecido [UNESP]
dc.contributor.authorEler, Danilo Medeiros [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-12-11T17:27:47Z
dc.date.available2018-12-11T17:27:47Z
dc.date.issued2016-04-01
dc.description.abstractSoftware 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.en
dc.description.affiliationDepartment of Statistics Applied Mathematics and Computer Science Unesp - Universidade Estadual Paulista
dc.description.affiliationDepartment of Mathematics and Computer Science Unesp - Universidade Estadual Paulista
dc.description.affiliationUnesp - Universidade Estadual Paulista
dc.description.affiliationUnespDepartment of Statistics Applied Mathematics and Computer Science Unesp - Universidade Estadual Paulista
dc.description.affiliationUnespDepartment of Mathematics and Computer Science Unesp - Universidade Estadual Paulista
dc.description.affiliationUnespUnesp - Universidade Estadual Paulista
dc.format.extent555-568
dc.identifierhttp://dx.doi.org/10.1007/978-3-319-32467-8_49
dc.identifier.citationAdvances in Intelligent Systems and Computing, v. 448, p. 555-568.
dc.identifier.doi10.1007/978-3-319-32467-8_49
dc.identifier.issn2194-5357
dc.identifier.lattes5693860025538327
dc.identifier.orcid0000-0002-1123-9784
dc.identifier.scopus2-s2.0-84962626192
dc.identifier.urihttp://hdl.handle.net/11449/177936
dc.language.isoeng
dc.relation.ispartofAdvances in Intelligent Systems and Computing
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectBSC
dc.subjectInformation visualization
dc.subjectMachine learning
dc.subjectMPS-SW
dc.subjectPerformance indicators
dc.subjectSoftware processes
dc.subjectSoftware quality
dc.titlePerformance indicators analysis in software processes using semi-supervised learning with information visualizationen
dc.typeCapítulo de livro
unesp.author.lattes5693860025538327[3]
unesp.author.orcid0000-0002-1123-9784[3]
unesp.departmentMatemática e Computação - FCTpt

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Artigos - Estatistica, Matemática Aplicada e Computação - IGCE
Presidente Prudente - FCT - Faculdade de Ciências e Tecnologia