Algorithmic Optimisation Method for Improving Use Case Points Estimation

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.

Parametric Software Effort Estimation Based on Optimizing Correction Factors and Multiple Linear Regression

“…• OCF [39] • UCP [20] • AOM [53] To evaluate the estimation accuracy, we experimented with five different runs (5-fold cross-validation). The comparisons of the effort estimation accuracy of each method are then based on the average results of these five runs.…”

“…Chiu et al [57] studied the effect of a genetic algorithm for adjusting the reused effort based on the distance between pairs of projects. The last group applies regression models such as linear, nonlinear, and stepwise models [50]- [53]. Regression models can provide higher accuracy for effort estimation by examining the validity of UCP variables.…”

“…Silhavy et al [53] developed the Algorithmic Optimisation Method (AOM) to increase the accuracy of the correction coefficients of the effort estimation process. The proposed method uses multiple least squares regression with all UCP elements.…”

Inferring Productivity Factor for Use Case Point Method

“…Schneider and Winters [7] proposed a method based on counting number of environmental factors. Additionally, the work of Silhavy et al [8] propose a new algorithm for calibration of productivity factor based on historical data. The best way to estimate this value is through analysis of previous completed projects for each software organization.…”

Using improved analytical programming algorithm for effort estimation in software engineering

“…Another enhancement could be the construction investigation and simplification of the Use Case Points method presented by Ochodek et al [6]. The recent work of Silhavy et al [7] suggest a new approach " automatic complexity estimation based on requirements ", which is partly based on Use Case Points method. Very promising way is a research of Kocaguneli et al [8], this paper shows, that ensemble of effort estimation methods could provide better results than a single estimator.…”