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Software effort estimation using machine learning technique

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 machinel...

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Main Authors:	Rahman, Mizanur, Roy, Partha Protim, Ali, Mohammad, Gonçalves, Teresa, Sarwar, Hasan
Format:	Article
Language:	English English
Published:	Science and Information Organization 2023
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/38683/1/Software%20effort%20estimation%20using%20machine%20learning%20technique.pdf http://umpir.ump.edu.my/id/eprint/38683/2/Software%20effort%20estimation%20using%20machine%20learning%20technique_ABS.pdf http://umpir.ump.edu.my/id/eprint/38683/ https://doi.org/10.14569/IJACSA.2023.0140491 https://doi.org/10.14569/IJACSA.2023.0140491
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id	my.ump.umpir.38683
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spelling	my.ump.umpir.386832023-10-31T07:47:10Z http://umpir.ump.edu.my/id/eprint/38683/ Software effort estimation using machine learning technique Rahman, Mizanur Roy, Partha Protim Ali, Mohammad Gonçalves, Teresa Sarwar, Hasan QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) 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 machinelearning 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 Science and Information Organization 2023 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/38683/1/Software%20effort%20estimation%20using%20machine%20learning%20technique.pdf pdf en http://umpir.ump.edu.my/id/eprint/38683/2/Software%20effort%20estimation%20using%20machine%20learning%20technique_ABS.pdf Rahman, Mizanur and Roy, Partha Protim and Ali, Mohammad and Gonçalves, Teresa and Sarwar, Hasan (2023) Software effort estimation using machine learning technique. International Journal of Advanced Computer Science and Applications, 14 (4). pp. 822-827. ISSN 2158-107X. (Published) https://doi.org/10.14569/IJACSA.2023.0140491 https://doi.org/10.14569/IJACSA.2023.0140491
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English English
topic	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General)
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Rahman, Mizanur Roy, Partha Protim Ali, Mohammad Gonçalves, Teresa Sarwar, Hasan Software effort estimation using machine learning technique
description	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 machinelearning 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
format	Article
author	Rahman, Mizanur Roy, Partha Protim Ali, Mohammad Gonçalves, Teresa Sarwar, Hasan
author_facet	Rahman, Mizanur Roy, Partha Protim Ali, Mohammad Gonçalves, Teresa Sarwar, Hasan
author_sort	Rahman, Mizanur
title	Software effort estimation using machine learning technique
title_short	Software effort estimation using machine learning technique
title_full	Software effort estimation using machine learning technique
title_fullStr	Software effort estimation using machine learning technique
title_full_unstemmed	Software effort estimation using machine learning technique
title_sort	software effort estimation using machine learning technique
publisher	Science and Information Organization
publishDate	2023
url	http://umpir.ump.edu.my/id/eprint/38683/1/Software%20effort%20estimation%20using%20machine%20learning%20technique.pdf http://umpir.ump.edu.my/id/eprint/38683/2/Software%20effort%20estimation%20using%20machine%20learning%20technique_ABS.pdf http://umpir.ump.edu.my/id/eprint/38683/ https://doi.org/10.14569/IJACSA.2023.0140491 https://doi.org/10.14569/IJACSA.2023.0140491
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score	13.235362

Software effort estimation using machine learning technique

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