Nonlinear regression models for software size estimation of Data Science and Machine Learning Java-applications
Oleksandr S. Oriekhov, Тetyana A. Farionova, Liubava S. Chernova, Lyudmila Chеrnovа, Mykhaіlo Vorona · 2024
his paper introduces the usage of regression models and equations for Data Science and Machine Learning Java applications size estimation. Size estimation of applications plays one of the key planning tasks at the early stages of project planning for the successful implementation of software development projects. Application size estimation is used to predict software development effort estimation using parametric models such as COCOMO, COCOMO II, etc. The aim of the study is to increase the reliability and accuracy of size estimation of Data Science and Machine Learning Java applications at the early stage of software project planning using class diagram metrics by building a nonlinear regression model. The object of research is the process of size estimation for open-source Data Science and Machine Learning Java applications. The subject of the study is the regression equations and nonlinear regression models to estimate the software size. To achieve this goal, we analyzed and compared the existing mathematical regression models and equations for Java applications size estimating on the sample of code metrics information from open-source Java applications of Data Science and Machine Learning. Proven the necessity of building the the three-factor nonlinear regression model for estimating the software size of Data Science and Machine Learning Java applications on the basis of the decimal logarithm normalizing transformation using the software code metrics such as the total quantity of classes, the total visible methods quantity, and the average fields quantity per class. The obtained nonlinear regression model is compared with the existing models by the regression models quality criteria such as the determination coefficient, mean magnitude of relative error and the percentage of prediction of the relative error level 0.25. The comparison confirms increasing the accuracy of software size estimation using the given sample by the obtained nonlinear regression model.