NON-LINEAR REGRESSION MODEL TO ESTIMATE THE SOFTWARE SIZE OF VB-BASED INFORMATION SYSTEMS
DOI:
https://doi.org/10.31649/1999-9941-2018-43-3-37-42Keywords:
non-linear regression model, confidence interval, prediction interval, software size estimation, VB-based system, normalizing transformation, non-Gaussian dataAbstract
The non-linear regression model to estimate the software size of VB-based information systems is constructed on the basis of normalization of the four-dimensional non-Gaussian data set (actual software size in the thousand lines of code, the total number of classes, the total number of relationships and the average number of attributes per class in conceptual data model from 32 systems) by the Johnson multivariate transformation for SB family. Comparison of the constructed model with the linear regression model and non-linear regression models based on the decimal logarithm and the Johnson univariate transformation is performed. Thе constructed model, in comparison with other regression models (both linear and non-linear), 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 of non-linear regression.
References
H. B. K. Tan, Y. Zhao, and H. Zhang, “Estimating LOC for information systems from their conceptual data models,” in Proc. of the 28th International Conference on Software Engineering (ICSE '06), Shanghai, China, 2006, p. 321-330.
H. B. K. Tan et al., “Conceptual data model-based software size estimation for information systems,” Transactions on Software Engineering and Methodology, vol. 19, issue 2, article No. 4, October. 2009.
D. M. Bates, and D. G. Watts, Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons, 1988.
G.A.F. Seber, and C.J. Wild, Nonlinear Regression. New York: John Wiley & Sons, 1989.
T. P. Ryan, Modern regression methods. New York: John Wiley & Sons, 1997.
N. R. Drapper, and H. Smith, Applied Regression Analysis. New York: John Wiley & Sons, 1998.
R. A. Johnson, and D. W. Wichern, Applied Multivariate Statistical Analysis. Pearson Prentice Hall, 2007.
S. Chatterjee, and J. S. Simonoff, Handbook of Regression Analysis. New York: John Wiley & Sons, 2013.
Natalia Prykhodko, and Sergiy Prykhodko, “Constructing the non-linear regression models on the basis of multivariate normalizing transformations” in Збірка праць конференції Моделювання-2018, Київ: ВД “Академперіодика” НАН України, 2018, с. 217-220.
S. Prykhodko, N. Prykhodko, L. Makarova, and A. Pukhalevych, “Application of the Squared Mahalanobis Distance for Detecting Outliers in Multivariate Non-Gaussian Data,” in Proc. of 14th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). Lviv-Slavske, Ukraine, 2018, p. 962-965.
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