Complexity Classification of Object-Oriented Projects Based on Class Model Information Using Quasi-Opposition Rao Algorithm-Based Neural Networks-Reference-Cited by-同舟云学术

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Complexity Classification of Object-Oriented Projects Based on Class Model Information Using Quasi-Opposition Rao Algorithm-Based Neural Networks

Published:2022 Issue: Volume: Page:141-150
ISSN:2190-3018
Container-title:Biologically Inspired Techniques in Many Criteria Decision Making
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Author:

Sahoo Pulak,Sanjeev Kumar Dash Ch.,Dehuri Satchidananda,Mohanty J. R.

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-8739-6_12

Reference10 articles.

1. Costagliola, G., Ferrucci, F., Tortora, G., Vitiello, G.: Class point: an approach for the size estimation of object-oriented systems. IEEE Trans. Software Eng. 31(1), 52–74 (2005)

2. Sang Eun, K., Lively, W., William, M., Simmons, D.B.: An effort estimation by UML points in early stage of software development. Softw. Eng. Res. Pract. 415–421 (2006)

3. Sahoo, P., Mohanty, J.R.: Early test effort prediction using UML diagrams. Indonesian J. Electr. Eng. Comput. Sci. 5, 220–228 (2017)

4. Sahoo, P., Mohanty, J.R.: Early System Test Effort Estimation Automation for Object-Oriented Systems, pp. 325–333. Information and Decision Sciences, Springer (2018)

5. Sahoo, P., Mohanty, J.R.: Test effort estimation in early stages using use case and class models for web applications. Int. J. Knowl.-Based Intel. Eng. Syst. 22(1), 215–229 (2018)

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