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DOI: 10.14569/IJACSA.2023.0140491
PDF

Software Effort Estimation using Machine Learning Technique

Author 1: Mizanur Rahman
Author 2: Partha Protim Roy
Author 3: Mohammad Ali
Author 4: Teresa Gonc¸alves
Author 5: Hasan Sarwar

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 4, 2023.

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Abstract: Software engineering effort estimation plays a significant role in managing project cost, quality, and time and creating software. Researchers have been paying close attention to software estimation during the past few decades, and a great amount of work has been done utilizing a variety of machine-learning techniques and algorithms. In order to better effectively evaluate predictions, this study recommends various machine learning algorithms for estimating, including k-nearest neighbor regression, support vector regression, and decision trees. These methods are now used by the software development industry for software estimating with the goal of overcoming the limitations of parametric and conventional estimation techniques and advancing projects. Our dataset, which was created by a software company called Edusoft Consulted LTD, was used to assess the effectiveness of the established method. The three commonly used performance evaluation measures, mean absolute error (MAE), mean squared error (MSE), and R square error, represent the base for these. Comparative experimental results demonstrate that decision trees perform better at predicting effort than other techniques.

Keywords: Software effort estimation; K-nearest neighbor re-gression; machine learning; decision tree; support vector regression

Mizanur Rahman, Partha Protim Roy, Mohammad Ali, Teresa Gonc¸alves and Hasan Sarwar, “Software Effort Estimation using Machine Learning Technique” International Journal of Advanced Computer Science and Applications(IJACSA), 14(4), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140491

@article{Rahman2023,
title = {Software Effort Estimation using Machine Learning Technique},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140491},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140491},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {4},
author = {Mizanur Rahman and Partha Protim Roy and Mohammad Ali and Teresa Gonc¸alves and Hasan Sarwar}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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