MATHEMATICAL MODELING IN PROBLEMS OF ESTIMATING THE SIZE OF APPLICABLE OPEN-SOURCE SOFTWARE IN C #

Abstract

Estimating the size of software, including open-source applications in C#, is currently an especially important part of the development of such projects. Knowing the size of application software in the initial stages of development and using models such as COCOMO, COCOMO II or methods and models of algorithmic modeling and others that perform calculations based on quantitative values of application software it can be calculated labor costs and project cost and anticipate risks related to development. In this paper it was analyzed existing regression equations for estimating the software size of different programming languages which in turn indicated the need to construct a nonlinear regression equation for open-source application software in C# using the universal one-dimensional Johnson normalizing transformation for the SB family, which allowed to construct a nonlinear regression equation of high quality (R2 = 0.974, MMRE = 0.198, PRED(0.25) = 0.778), and which has better performance compared to the linear regression equation without using of normalizing transformations for empirical data (R2 = 0.887, MMRE =1.028, PRED(0.25) = 0.361), as well as in compared with the nonlinear regression equation using the natural logarithm as a normalizing transformation (R2 = 0.819, MMRE = 0.222, PRED(0.25) = 0.694). In addition, the use of the universal one-dimensional normalizing Johnson transformation of the SB family made it possible to construct a narrower confidence interval and prediction interval for the nonlinear regression equation compared to the intervals that were constructed using the natural logarithm as a normalizing transformation. As a result, a software application was developed for estimating the size of open source application software in C#, using a nonlinear regression equation based on the universal one-dimensional Johnson normalizing transformation for the SB family, which automates the calculation process and simplifies the using constructed nonlinear regression equations that reduced the time of relevant calculations and reduced the number of calculation errors.