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Algorithmic optimisation method for improving use case points estimation

机译:改进用例点估计的算法优化方法

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摘要

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.
机译:本文提出了一种新的大小估计方法,可用于估计软件工程项目的大小级别。算法优化方法基于用例点和多重最小二乘回归。该方法分为三个阶段。第一阶段处理用例点和校正系数值的计算。校正系数是通过使用多元最小二乘回归获得的。在第二和第三阶段中估计新项目。在第二阶段中,建立用于新估计的用例点参数,在第三阶段中,执行项目估计。通过使用新开发的估计方程获得最终估计,该方程使用了两个校正系数。根据算法优化方法的相对误差评分的大小,它们的性能比用例点方法高约43%。所有结果均通过标准方法进行评估:外观检查,拟合优度和统计学意义。

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