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Algorithmic Optimisation Method for Improving Use Case Points Estimation
This paper presents a new size estimation method that can be used to estimate size level for software engineering projects. The Algorithmic Optimisation Method is based on Use Case Points and on Multiple Least Square Regression. The method is derived into three phases. The first phase deals with cal...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4638361/ https://www.ncbi.nlm.nih.gov/pubmed/26550835 http://dx.doi.org/10.1371/journal.pone.0141887 |
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author | Silhavy, Radek Silhavy, Petr Prokopova, Zdenka |
author_facet | Silhavy, Radek Silhavy, Petr Prokopova, Zdenka |
author_sort | Silhavy, Radek |
collection | PubMed |
description | This paper presents a new size estimation method that can be used to estimate size level for software engineering projects. The Algorithmic Optimisation Method is based on Use Case Points and on Multiple Least Square Regression. The method is derived into three phases. The first phase deals with calculation Use Case Points and correction coefficients values. Correction coefficients are obtained by using Multiple Least Square Regression. New project is estimated in the second and third phase. In the second phase Use Case Points parameters for new estimation are set up and in the third phase project estimation is performed. Final estimation is obtained by using newly developed estimation equation, which used two correction coefficients. The Algorithmic Optimisation Method performs approximately 43% better than the Use Case Points method, based on their magnitude of relative error score. All results were evaluated by standard approach: visual inspection, goodness of fit measure and statistical significance. |
format | Online Article Text |
id | pubmed-4638361 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-46383612015-11-13 Algorithmic Optimisation Method for Improving Use Case Points Estimation Silhavy, Radek Silhavy, Petr Prokopova, Zdenka PLoS One Research Article This paper presents a new size estimation method that can be used to estimate size level for software engineering projects. The Algorithmic Optimisation Method is based on Use Case Points and on Multiple Least Square Regression. The method is derived into three phases. The first phase deals with calculation Use Case Points and correction coefficients values. Correction coefficients are obtained by using Multiple Least Square Regression. New project is estimated in the second and third phase. In the second phase Use Case Points parameters for new estimation are set up and in the third phase project estimation is performed. Final estimation is obtained by using newly developed estimation equation, which used two correction coefficients. The Algorithmic Optimisation Method performs approximately 43% better than the Use Case Points method, based on their magnitude of relative error score. All results were evaluated by standard approach: visual inspection, goodness of fit measure and statistical significance. Public Library of Science 2015-11-09 /pmc/articles/PMC4638361/ /pubmed/26550835 http://dx.doi.org/10.1371/journal.pone.0141887 Text en © 2015 Silhavy et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Silhavy, Radek Silhavy, Petr Prokopova, Zdenka Algorithmic Optimisation Method for Improving Use Case Points Estimation |
title | Algorithmic Optimisation Method for Improving Use Case Points Estimation |
title_full | Algorithmic Optimisation Method for Improving Use Case Points Estimation |
title_fullStr | Algorithmic Optimisation Method for Improving Use Case Points Estimation |
title_full_unstemmed | Algorithmic Optimisation Method for Improving Use Case Points Estimation |
title_short | Algorithmic Optimisation Method for Improving Use Case Points Estimation |
title_sort | algorithmic optimisation method for improving use case points estimation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4638361/ https://www.ncbi.nlm.nih.gov/pubmed/26550835 http://dx.doi.org/10.1371/journal.pone.0141887 |
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