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Algorithmic Optimisation Method for Improving Use Case Points Estimation

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  • Radek Silhavy
  • Petr Silhavy
  • Zdenka Prokopova

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.

Suggested Citation

  • Radek Silhavy & Petr Silhavy & Zdenka Prokopova, 2015. "Algorithmic Optimisation Method for Improving Use Case Points Estimation," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
    • Handle: RePEc:plo:pone00:0141887
    DOI: 10.1371/journal.pone.0141887
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    Cited by:

    1. Hoc Huynh Thai & Petr Silhavy & Martin Fajkus & Zdenka Prokopova & Radek Silhavy, 2022. "Propose-Specific Information Related to Prediction Level at x and Mean Magnitude of Relative Error: A Case Study of Software Effort Estimation," Mathematics, MDPI, vol. 10(24), pages 1-14, December.

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