A NONLINEAR REGRESSION MODEL TO ESTIMATE THE SIZE OF WEB APPS CREATED USING THE CAKEPHP FRAMEWORK
DOI:
https://doi.org/10.15588/1607-3274-2021-4-12Keywords:
software size estimation, Web app, nonlinear regression model, normalizing transformation, non-Gaussian data.Abstract
Context. The problem of estimating the software size in the early stage of a software project is important because a software size estimate is used for predicting the software development effort, including Web apps created using the CakePHP framework. The object of the study is the process of estimating the size of Web apps created using the CakePHP framework. The subject of the study is the nonlinear regression models to estimate the size of Web apps created using the CakePHP framework.
Objective. The goal of the work is the building the nonlinear regression model with three predictors for estimating the size of Web apps created using the CakePHP framework on the basis of the Box-Cox four-variate normalizing transformation to increase the confidence in early size estimation of these apps.
Method. The model, confidence and prediction intervals of multiply nonlinear regression to estimate the size of Web apps created using the CakePHP framework are constructed based on the Box-Cox multivariate normalizing transformation for non-Gaussian data with the help of appropriate techniques. The techniques to build the models, confidence, and prediction intervals of nonlinear regressions are based on the multiple nonlinear regression analysis using the multivariate normalizing transformations. The techniques allow taking into account the correlation between dependent and independent variables in the case of normalization of multivariate non-Gaussian data. In general, this leads to a reduction of the mean magnitude of relative error, the widths of the confidence, and prediction intervals in comparison with nonlinear models constructed using univariate normalizing transformations.
Results. Comparison of the constructed model with the nonlinear regression models based on the decimal logarithm and the BoxCox univariate transformation has been performed.
Conclusions. The nonlinear regression model with three predictors to estimate the size of Web apps created using the CakePHP framework is constructed on the basis of the Box-Cox four-variate transformation. This model, in comparison with other nonlinear regression models, has a larger multiple coefficient of determination, a smaller value of the mean magnitude of relative error and smaller widths of the confidence and prediction intervals. The prospects for further research may include the application of other multivariate normalizing transformations and data sets to construct the nonlinear regression model to estimate the size of Web apps created using the other frameworks.
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