Early size estimation of web apps created using codeigniter framework by nonlinear regression models

Abstract

Subject matter: Early software size estimation is one of the project managers' significant problems in evaluating app development efforts because software size is the major determinant of software project effort. Function points (FPs) and lines of code (LOC) are most commonly used as measures of size in existing software effort estimation methods and models. As is known, both these metrics have their advantages and disadvantages when used for software effort estimation. Although the FPs-based measure has the advantage over the LOC in that it does not depend on the technologies used, however, the assessment of efforts requires considering such factors (environmental factors). Considering the above factors can be ensured by appropriate models for estimating the LOC-based effort. Nowadays, many Web apps are created using PHP frameworks making the app development faster. CodeIgniter is one such powerful framework. However, there are no regression models for estimating the software size of Web apps created using the CodeIgniter framework. This requires the construction of the appropriate models. The task of this paper is to develop a nonlinear regression model for estimating the software size (in KLOC, kilo lines of code) of Web apps created using the CodeIgniter framework. Method: We apply the technique for constructing nonlinear regression models based on the multivariate normalizing transformations and prediction intervals. The result is three nonlinear regression models with three predictors: the total number of classes, the average number of methods per class, and the DIT (Depth of Inheritance Tree) average per class. To build these models for estimating the size of Web apps created using the CodeIgniter framework, we used three well-known normalizing transformations: two univariate transformations (the decimal logarithm and the Box-Cox transformation) and the Box-Cox four-variate transformation. Conclusions. The nonlinear regression model constructed by the Box-Cox four-variate transformation has better size prediction results than other regression models based on the univariate transformations.