Estimating the Efforts of Mobile Application Development in the Planning Phase Using Nonlinear Regression Analysis

Prykhodko, Sergiy; Prykhodko, Natalia; Knyrik, Kateryna

doi:10.2478/acss-2020-0019

Abstract

The authors consider the construction of a nonlinear multiple regression model, its confidence and prediction intervals to evaluate the efforts of mobile application development in the planning phase based on the multivariate normalizing transformation and outlier detection. The constructed model is compared to the linear regression model and nonlinear regression models based on the univariate transformations, such as the decimal logarithm, Box–Cox, and Johnson transformation. This model, in comparison with other regression models, has better prediction accuracy.

References

[1] M. Rouse, “Mobile application development,” March 2019. [Online]. Available: http://searchmicroservices.techtarget.com/definition/mobileapplication-development [Accessed: Nov. 19, 2020].
Search in Google Scholar Back to article
[2] T. P. Ryan, Modern regression methods, 2^nd ed. New York: John Wiley & Sons, 2008. https://doi.org/10.1002/978047038280610.1002/9780470382806
Search in Google Scholar Back to article
[3] S. Chatterjee and J. S. Simonoff, Handbook of regression analysis. New York: John Wiley & Sons, 2013. https://doi.org/10.1002/978111853284310.1002/9781118532843
Search in Google Scholar Back to article
[4] N. R. Drapper and H. Smith, Applied regression analysis, 3^rd ed. New York: John Wiley & Sons, 1998. https://doi.org/10.1002/978111862559010.1002/9781118625590
Search in Google Scholar Back to article
[5] T. Arnuphaptrairong and W. Suksawasd, “An empirical validation of mobile application effort estimation models,” in Proceedings of the International MultiConference of Engineers and Computer Scientists (IMECS 2017), 2017, pp. 697–701.
Search in Google Scholar Back to article
[6] R. Francese, C. Gravino, M. Risi, G. Scanniello and G. Tortora, “On the use of requirements measures to predict software project and product measures in the context of Android mobile apps: A preliminary study,” in Proceedings of the 41st Euromicro Conference on Software Engineering and Advanced Applications (SEAA 2015), 2015, pp. 357–364. https://doi.org/10.1109/SEAA.2015.2210.1109/SEAA.2015.22
Search in Google Scholar Back to article
[7] S. A. Shahwaiz, A. A. Malik and N. Sabahat, “A parametric effort estimation model for mobile apps,” in Proceedings of the 19th International Multi-Topic Conference (INMIC 2016), 2016, pp. 1–6. https://doi.org/10.1109/INMIC.2016.784011410.1109/INMIC.2016.7840114
Search in Google Scholar Back to article
[8] S. Prykhodko, N. Prykhodko, K. Knyrik and A. Pukhalevych, “Mathematical modeling of effort of mobile application development in a planning phase,” in Proceedings of the 1st International Workshop on Information-Communication Technologies & Embedded Systems. CEUR Workshop Proceedings, vol. 2516, 2019, pp. 96–105.
Search in Google Scholar Back to article
[9] S. Prykhodko, N. Prykhodko, L. Makarova and A. Pukhalevych, “Application of the squared Mahalanobis distance for detecting outliers in multivariate non-Gaussian data,” in Proceedings of the 14th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), 2018, pp. 962–965. https://doi.org/10.1109/TCSET.2018.833635310.1109/TCSET.2018.8336353
Search in Google Scholar Back to article
[10] T. Foss, E. Stensrud, B. Kitchenham and I. Myrtveit, “A simulation study of the model evaluation criterion MMRE,” IEEE Transactions on software engineering, vol. 11, no. 29, pp. 985–995, 2003. https://doi.org/10.1109/TSE.2003.124530010.1109/TSE.2003.1245300
Search in Google Scholar Back to article
[11] D. Port and M. Korte, “Comparative studies of the model evaluation criterions MMRE and PRED in software cost estimation research,” in Proceedings of the 2nd ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, 2008, pp. 51–60. https://doi.org/10.1145/1414004.141401510.1145/1414004.1414015
Search in Google Scholar Back to article
[12] G. E. P. Box and D. R. Cox, “An analysis of transformations,” Journal of the Royal Statistical Society: Series B (Methodological), vol. 2, no. 26, pp. 211–252, 1964. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x10.1111/j.2517-6161.1964.tb00553.x
Search in Google Scholar Back to article
[13] R. A. Johnson and D. W. Wichern, Applied multivariate statistical analysis. 6^th ed. Pearson Prentice Hall, 2007.
Search in Google Scholar Back to article
[14] N. Prykhodko, S. Prykhodko and M. Vorona, “The non-linear regression model to estimate the part of NPLS in the whole loan portfolio of Ukrainian banks,” in 2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC), 2018, pp. 261–265. https://doi.org/10.1109/SAIC.2018.851689910.1109/SAIC.2018.8516899
Search in Google Scholar Back to article
[15] I. Olkin and A. R. Sampson, “Multivariate Analysis: Overview,” in Smelser, N. J., Baltes, P. B. (eds.) International Encyclopedia of Social & Behavioral Sciences. Pergamon, 2001, pp. 10240–10247. https://doi.org/10.1016/B0-08-043076-7/00472-110.1016/B0-08-043076-7/00472-1
Search in Google Scholar Back to article
[16] K. V. Mardia, “Measures of multivariate skewness and kurtosis with applications,” Biometrika, vol. 57, no. 3, pp. 519–530, 1970. https://doi.org/10.1093/biomet/57.3.51910.1093/biomet/57.3.519
Search in Google Scholar Back to article

Estimating the Efforts of Mobile Application Development in the Planning Phase Using Nonlinear Regression Analysis

Abstract

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