NONLINEAR REGRESSION MODELS FOR ESTIMATING THE DURATION OF SOFTWARE DEVELOPMENT IN JAVA FOR PC BASED ON THE 2021 ISBSG DATA

Context. The problem of estimating the duration of software development in Java for personal computers (PC) is important because, first, failed duration estimating is often the main contributor to failed software projects, second, Java is a popular language, and, third, a personal computer is a widespread multi-purpose computer. The object of the study is the process of estimating the duration of software development in Java for PC. The subject of the study is the nonlinear regression models to estimate the duration of software development in Java for PC. Objective. The goal of the work is to build nonlinear regression models for estimating the duration of software development in Java for PC based on the normalizing transformations and deleting outliers in data to increase the confidence of the estimation in comparison to the ISBSG model for the PC platform. Method. The models, confidence, and prediction intervals of nonlinear regressions to estimate the duration of software development in Java for PC are constructed based on the normalizing transformations 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 normalizing transformations. Also, we apply outlier removal for model construction. In general, the above leads to a reduction of the mean magnitude of relative error, the widths of the confidence, and prediction intervals in comparison to nonlinear models constructed without outlier removal application in the model construction process. Results. A comparison of the model based on the decimal logarithm transformation with the nonlinear regression models based on the Johnson (for the SB family) and Box-Cox transformations as both univariate and bivariate ones has been performed. Conclusions. The nonlinear regression model to estimate the duration of software development in Java for PC is constructed based on the decimal logarithm transformation. This model, in comparison with other nonlinear regression models, has smaller widths of the confidence and prediction intervals for effort values that are bigger than 900 person-hours. The prospects for further research may include the application of bivariate normalizing transformations and data sets to construct the nonlinear regression models for estimating the duration of software development in other languages for PC and other platforms, for example, mainframe.