Algorithmic Optimisation Method for Improving Use Case Points Estimation

Radek Silhavy, Petr Silhavy and Zdenka Prokopova

PLOS ONE, 2015, vol. 10, issue 11, 1-14

Abstract: 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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0141887

DOI: 10.1371/journal.pone.0141887

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