Review and Empirical Analysis of Machine Learning-Based Software Effort Estimation-Reference-Cited by-同舟云学术

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Review and Empirical Analysis of Machine Learning-Based Software Effort Estimation

Published:2024 Issue: Volume:12 Page:85661-85680
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Rahman Mizanur¹^ORCID,Sarwar Hasan²^ORCID,Kader MD. Abdul³^ORCID,Gonçalves Teresa⁴^ORCID,Tin Ting Tin⁵^ORCID

Affiliation:

1. School of Computer Science, Western Illinois University, Macomb, IL, USA

2. Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh

3. Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia

4. Departamento de Informática, Universidade de Évora, Évora, Portugal

5. Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia

Funder

Institute for Advanced Research Publication Grant of United International University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10538131.pdf?arnumber=10538131

Reference76 articles.

1. Software effort estimation accuracy prediction of machine learning techniques: A systematic performance evaluation

2. Software development effort estimation: a systematic mapping study

3. Empirical Investigation About the Factors Affecting the Cost Estimation in Global Software Development Context

4. Analysis of Software Effort Estimation by Machine Learning Techniques

5. Heterogeneous Ensemble Model to Optimize Software Effort Estimation Accuracy

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