Performance Analysis of Machine Learning Approaches in Software Complexity Prediction-Reference-Cited by-同舟云学术

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Performance Analysis of Machine Learning Approaches in Software Complexity Prediction

Published:2020-12-17 Issue: Volume: Page:27-39
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
Short-container-title:

Author:

Moshin Reza Sayed,Mahfujur Rahman Md.,Parvez Hasnat,Badreddin Omar,Al Mamun Shamim

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-33-4673-4_3

Reference23 articles.

1. Alakus, T.B., Das, R., Turkoglu, I.: An overview of quality metrics used in estimating software faults. In: 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1–6. IEEE (2019)

2. Ogheneovo, E.E., et al.: On the relationship between software complexity and maintenance costs. J. Comput. Commun. 2(14), 1 (2014)

3. Yu, S., Zhou, S.: A survey on metric of software complexity. In: 2010 2nd IEEE International Conference on Information Management and Engineering, pp. 352–356. IEEE (2010)

4. Reza, S.M., Rahman, M.M., Parvez, M.H., Shamim Kaiser, M., Mamun, S.A.: Innovative approach in web application effort & cost estimation using functional measurement type. In: 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), pp. 1–7. IEEE (2015)

5. Durdik, Z., Klatt, B., Koziolek, H., Krogmann, K., Stammel, J., Weiss, R.: Sustainability guidelines for long-living software systems. In: 2012 28th IEEE International Conference on Software Maintenance (ICSM), pp. 517–526. IEEE (2012)

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