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Segmented software cost estimation models based on fuzzy clustering


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dc.creator Aroba Páez, Javier es
dc.creator Cuadrado Gallego, Juan J. es
dc.creator Sicilia, Miguel Ángel es
dc.creator Ramos Román, Isabel es
dc.creator García Barriocanal, Elena es 2017-05-05T10:50:12Z 2017-05-05T10:50:12Z 2008
dc.identifier.citation Aroba Páez, J., Cuadrado Gallego, J.J., Sicilia, M.Á., Ramos Román, I. y García Barriocanal, E. (2008). Segmented software cost estimation models based on fuzzy clustering. Journal of Systems and Software, 81 (11), 1944-1950.
dc.identifier.issn 0164-1212 es
dc.description.abstract Parametric software cost estimation models are based on mathematical relations, obtained from the study of historical software projects databases, that intend to be useful to estimate the effort and time required to develop a software product. Those databases often integrate data coming from projects of a heterogeneous nature. This entails that it is difficult to obtain a reasonably reliable single parametric model for the range of diverging project sizes and characteristics. A solution proposed elsewhere for that problem was the use of segmented models in which several models combined into a single one contribute to the estimates depending on the concrete characteristic of the inputs. However, a second problem arises with the use of segmented models, since the belonging of concrete projects to segments or clusters is subject to a degree of fuzziness, i.e. a given project can be considered to belong to several segments with different degrees. This paper reports the first exploration of a possible solution for both problems together, using a segmented model based on fuzzy clusters of the project space. The use of fuzzy clustering allows obtaining different mathematical models for each cluster and also allows the items of a project database to contribute to more than one cluster, while preserving constant time execution of the estimation process. The results of an evaluation of a concrete model using the ISBSG 8 project database are reported, yielding better figures of adjustment than its crisp counterpart. es
dc.description.sponsorship Ministerio de Ciencia y Tecnología TIN2004-06689-C03 es
dc.format application/pdf es
dc.language.iso eng es
dc.publisher Elsevier es
dc.relation.ispartof Journal of Systems and Software, 81 (11), 1944-1950.
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Internacional *
dc.rights.uri *
dc.subject Software effort estimation es
dc.subject Parametric software estimation models es
dc.subject Fuzzy clustering es
dc.title Segmented software cost estimation models based on fuzzy clustering es
dc.type info:eu-repo/semantics/article es
dc.type.version info:eu-repo/semantics/submittedVersion es
dc.rights.accessrights info:eu-repo/semantics/openAccess es
dc.contributor.affiliation Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos es
dc.relation.projectID TIN2004-06689-C03 es
dc.relation.publisherversion es
dc.identifier.doi 10.1016/j.jss.2008.01.016 es
idus.format.extent 7 es
dc.journaltitle Journal of Systems and Software es
dc.publication.volumen 81 es
dc.publication.issue 11 es
dc.publication.initialPage 1944 es
dc.publication.endPage 1950 es
dc.contributor.funder Ministerio de Ciencia y Tecnología (MCYT). España
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