DOI: https://doi.org/10.15588/1607-3274-2018-3-17

### THE NON-LINEAR REGRESSION MODEL TO ESTIMATE THE SOFTWARE SIZE OF OPEN SOURCE JAVA-BASED SYSTEMS

#### Abstract

obtained from estimating the software size is used for predicting the software development effort, including open-source Java-based

information systems. The object of the study is the process of estimating the software size of open-source Java-based information

systems. The subject of the study is the regression models for estimating the software size of open-source Java-based information

systems.

Objective. The goal of the work is the creation of the non-linear regression model for estimating the software size of open-source

Java-based information systems on the basis of the Johnson multivariate normalizing transformation.

Method. The model, confidence and prediction intervals of multiply non-linear regression for estimating the software size of

open-source Java-based information systems are constructed on the basis of the Johnson multivariate normalizing transformation for

non-Gaussian data with the help of appropriate techniques. The techniques to build the models, equations, confidence and prediction

intervals of non-linear regressions are based on the multiple non-linear regression analysis using the multivariate normalizing

transformations. The appropriate techniques are considered. The techniques allow to take into account the correlation between

random 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 the linear models or nonlinear

models constructed using univariate normalizing transformations.

Results. Comparison of the constructed model with the linear model and non-linear regression models based on the decimal

logarithm and the Johnson univariate transformation has been performed.

Conclusions. The non-linear regression model to estimate the software size of open-source Java-based information systems is

constructed on the basis of the Johnson multivariate transformation for SB family. This model, in comparison with other regression

models (both linear and non-linear), has a larger multiple coefficient of determination, a larger value of percentage of prediction and

a smaller value of the mean magnitude of relative error. The prospects for further research may include the application of other

multivariate normalizing transformations and data sets to construct the non-linear regression model for estimating the software size

of open-source Java-based information systems.

#### Keywords

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Kaczmarek J., Kucharski M. Size and effort estimation for

applications written in Java, Information and Software

Technology, 2004, Vol. 46, Issue 9, pp. 589–601. DOI:

1016/j.infsof.2003.11.001

Tan H. B. K., Zhao Y., Zhang H. Estimating LOC for

information systems from their conceptual data models,

Software Engineering : the 28th International Conference

(ICSE '06), Shanghai. China, May 20–28, 2006 :

proceedings, pp. 321–330. DOI: 10.1145/1134285.1134331

Tan H. B. K., Zhao Y., Zhang H. Conceptual data modelbased software size estimation for information systems,

Transactions on Software Engineering and Methodology,

, Vol. 19, Issue 2, October 2009, Article No. 4. DOI:

1145/1571629.1571630

Kiewkanya M., Surak S. Constructing C++ software size

estimation model from class diagram, Computer Science and

Software Engineering : 13th International Joint Conference,

Khon Kaen, Thailand, July 13–15, 2016 : proceedings,

pp. 1–6. DOI: 10.1109/JCSSE.2016.7748880

Bates D. M., Watts D. G. Nonlinear Regression Analysis

and Its Applications. New York, John Wiley & Sons, 1988,

p. DOI:10.1002/9780470316757

Seber G.A.F., Wild C. J. Nonlinear Regression. New York,

John Wiley & Sons, 1989, 768 p. DOI: 10.1002/0471725315

Ryan T.P. Modern regression methods. New York, John

Wiley & Sons, 1997, 529 p. DOI: 10.1002/9780470382806

Johnson R. A., Wichern D. W. Applied Multivariate

Statistical Analysis. Pearson Prentice Hall, 2007, 800 p.

Prykhodko S. B. Developing the software defect prediction

models using regression analysis based on normalizing

transformations, Modern Problems in Testing of the Applied

Software : the Research and Practice Seminar (PTTAS-

, Poltava, Ukraine, May 25–26, 2016 : abstracts,

pp. 6–7.

Stanfield P. M., Wilson J. R., Mirka G. A., Glasscock N. F.,

Psihogios J. P., Davis J. R. Multivariate input modeling with

Johnson distributions, The 28th Winter simulation

conference WSC’96, Coronado, CA, USA, December 8–11,

: proceedings, ed. S. Andradуttir, K. J. Healy,

D.H.Withers, and B. L. Nelson, IEEE Computer Society

Washington, DC, USA, 1996, pp. 1457–1464.

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