A MULTIPLE NON-LINEAR REGRESSION MODEL TO ESTIMATE THE AGILE TESTING EFFORTS FOR SMALL WEB PROJECTS
DOI:
https://doi.org/10.15588/1607-3274-2019-2-17Keywords:
agile testing, estimation, testing effort, Web project, multiple non-linear regression model, multivariate normalizing transformation, non-Gaussian data.Abstract
Context. Software testing effort estimation is one of the important problems in software development and software testing lifecycle. The object of the study is the process of estimating the agile testing efforts for small Web projects. The subject of the study is the multiple regression models for estimating the agile testing efforts for small Web projects.
Objective. The goal of the work is the creation of the multiple non-linear regression model for estimating the agile testing efforts
for small Web projects on the basis of the Johnson multivariate normalizing transformation.
Method. The model, confidence and prediction intervals of multiple non-linear regression for estimating the agile testing efforts
for small Web projects are constructed on the basis of the Johnson multivariate normalizing transformation for non-Gaussian data
with the help of appropriate techniques. The techniques based on the multiple non-linear regression analysis using the multivariate normalizing transformations to build the models, equations, confidence and prediction intervals of multiple non-linear regressions are used. 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 and 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 multiple non-linear regression model to estimate the agile testing efforts for small Web projects is firstly
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 smaller value of the mean magnitude of relative error, smaller widths of the confidence and prediction intervals. The prospects for further research may include the application of other multivariate normalizing transformations and data sets to construct the multiple non-linear regression model for estimating the agile testing efforts for small Web projects.
References
Olausson M., Rossberg J., Ehn J., Sköld M. Introduction to Agile Planning, Development, and Testing, Pro Team
Foundation Service. Berkeley, CA, Apress, 2013, Chapter 2, pp. 9–19. DOI: 10.1007/978-1-4302-5996-1_2
Olausson M., Rossberg J., Ehn J., Sköld M. Agile Testing, Pro Team Foundation Service. Berkeley, CA, Apress, 2013, Chapter
, pp. 19–33. DOI: 10.1007/978-1-4302-5996-1_19
Penmetsa J. R., Mohanty H., Mohanty J., Balakrishnan A. (eds). Agile Testing, Trends in Software Testing. Singapore, Springer,
, Chapter 2, pp. 19–33. DOI: 10.1007/978-981-10-1415-4_2
Nader-Rezvani N. Agile Quality Test Strategy, An Executive’s Guide to Software Quality in an Agile Organization. Berkeley,
CA, Apress, 2019, Chapter 7, pp. 121–138. DOI: 10.1007/978-1-4842-3751-9_7
Agile Testing – Principles, methods & advantages [Electronic resource]. Access mode: https://reqtest.com/testing-blog/agiletesting-principles-methods-advantages/
Boehm B. W., Abts C., Brown A. W. et al. Software Cost Estimation with COCOMO II, Upper Saddle River, NJ, Prentice
Hall PTR, 2000, 506 p.
Lazić L., Ðokić I., Milinković S. Challenges in estimating software testing effort, Proceedings of INFOTEH-JAHORINA,
, Vol. 13, pp. 637–642.
Bates D. M., Watts D. G. Nonlinear Regression Analysis and Its Applications. New York, John Wiley & Sons, 1988, 384 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.
Samprit Chatterjee, Jeffrey S. Somonoff. Chatterjee Samprit. Handbook of Regression Analysis. New York, John Wiley &
Sons, 2012, 252 p. DOI:10.1002/9781118532843
Prykhodko N. V., Prykhodko S. B. Constructing the non-linear regression models on the basis of multivariate normalizing
transformations, Electronic modeling, 2018, Vol. 40, No. 6, pp. 101–110. DOI: 10.15407/emodel.40.06.101
Prykhodko N. V., Prykhodko S. B. The non-linear regression model to estimate the software size of open source Java-based
systems, Radio Electronics, Computer Science, Control, 2018, No. 3 (46), pp. 158–166. DOI 10.15588/1607-3274-2018-3-17
Prykhodko S., Prykhodko N., Makarova L. et al. Detecting Outliers in Multivariate Non-Gaussian Data on the basis of
Normalizing Transformations / [S. Prykhodko, // Electrical and Computer Engineering : the 2017 IEEE First Ukraine
Conference (UKRCON) «Celebrating 25 Years of IEEE Ukraine Section». Kyiv, Ukraine, May 29–June 2, 2017,
proceedings. Kyiv, IEEE, 2017, pp. 846–849. DOI:10.1109/UKRCON.2017.8100366
Mardia K. V. Measures of multivariate skewness and kurtosis with applications, Biometrika, 1970, Vol. 57, pp. 519–530. DOI:
1093/biomet/57.3.519
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 N. V. Prykhodko, S. B. Prykhodko
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
-
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
-
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
-
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
