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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 6, 2021.
Abstract: This project involves research about software effort estimation using machine learning algorithms. Software cost and effort estimation are crucial parts of software project development. It determines the budget, time and resources needed to develop a software project. One of the well-established software project estimation models is Constructive Cost Model (COCOMO) which was developed in the 1980s. Even though such a model is being used, COCOMO has some weaknesses and software developers still facing the problem of lack of accuracy of the effort and cost estimation. Inaccuracy in the estimated effort will affect the schedule and cost of the whole project as well. The objective of this research is to use several algorithms of machine learning to estimate the effort of software project development. The best machine learning model is chosen to compare with the COCOMO.
Noor Azura Zakaria, Amelia Ritahani Ismail, Afrujaan Yakath Ali, Nur Hidayah Mohd Khalid and Nadzurah Zainal Abidin, “Software Project Estimation with Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 12(6), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120685
@article{Zakaria2021,
title = {Software Project Estimation with Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120685},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120685},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {6},
author = {Noor Azura Zakaria and Amelia Ritahani Ismail and Afrujaan Yakath Ali and Nur Hidayah Mohd Khalid and Nadzurah Zainal Abidin}
}
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.