Spectral Clustering Effect in Software Development Effort Estimation

Software development effort estimation is essential for software project planning and management. In this study, we present a spectral clustering algorithm based on symmetric matrixes as an option for data processing. It is expected that constructing an estimation model on more similar data can incr...

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Autores principales:	Petr Silhavy, Radek Silhavy, Zdenka Prokopova
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	clustering development effort estimation function point analysis software engineering software measurement spectral clustering Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/90225379b2fd455c8e5262ed071b9354
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spelling	oai:doaj.org-article:90225379b2fd455c8e5262ed071b93542021-11-25T19:06:53ZSpectral Clustering Effect in Software Development Effort Estimation10.3390/sym131121192073-8994https://doaj.org/article/90225379b2fd455c8e5262ed071b93542021-11-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2119https://doaj.org/toc/2073-8994Software development effort estimation is essential for software project planning and management. In this study, we present a spectral clustering algorithm based on symmetric matrixes as an option for data processing. It is expected that constructing an estimation model on more similar data can increase the estimation accuracy. The research methods employ symmetrical data processing and experimentation. Four experimental models based on function point analysis, stepwise regression, spectral clustering, and categorical variables have been conducted. The results indicate that the most advantageous variant is a combination of stepwise regression and spectral clustering. The proposed method provides the most accurate estimates compared to the baseline method and other tested variants.Petr SilhavyRadek SilhavyZdenka ProkopovaMDPI AGarticleclusteringdevelopment effort estimationfunction point analysissoftware engineeringsoftware measurementspectral clusteringMathematicsQA1-939ENSymmetry, Vol 13, Iss 2119, p 2119 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	clustering development effort estimation function point analysis software engineering software measurement spectral clustering Mathematics QA1-939
spellingShingle	clustering development effort estimation function point analysis software engineering software measurement spectral clustering Mathematics QA1-939 Petr Silhavy Radek Silhavy Zdenka Prokopova Spectral Clustering Effect in Software Development Effort Estimation
description	Software development effort estimation is essential for software project planning and management. In this study, we present a spectral clustering algorithm based on symmetric matrixes as an option for data processing. It is expected that constructing an estimation model on more similar data can increase the estimation accuracy. The research methods employ symmetrical data processing and experimentation. Four experimental models based on function point analysis, stepwise regression, spectral clustering, and categorical variables have been conducted. The results indicate that the most advantageous variant is a combination of stepwise regression and spectral clustering. The proposed method provides the most accurate estimates compared to the baseline method and other tested variants.
format	article
author	Petr Silhavy Radek Silhavy Zdenka Prokopova
author_facet	Petr Silhavy Radek Silhavy Zdenka Prokopova
author_sort	Petr Silhavy
title	Spectral Clustering Effect in Software Development Effort Estimation
title_short	Spectral Clustering Effect in Software Development Effort Estimation
title_full	Spectral Clustering Effect in Software Development Effort Estimation
title_fullStr	Spectral Clustering Effect in Software Development Effort Estimation
title_full_unstemmed	Spectral Clustering Effect in Software Development Effort Estimation
title_sort	spectral clustering effect in software development effort estimation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/90225379b2fd455c8e5262ed071b9354
work_keys_str_mv	AT petrsilhavy spectralclusteringeffectinsoftwaredevelopmenteffortestimation AT radeksilhavy spectralclusteringeffectinsoftwaredevelopmenteffortestimation AT zdenkaprokopova spectralclusteringeffectinsoftwaredevelopmenteffortestimation
_version_	1718410290216828928

Spectral Clustering Effect in Software Development Effort Estimation

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