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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
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Science and Information Organization
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/38683/ 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 |
| _version_ | 1848825570920497152 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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 |
| first_indexed | 2025-11-15T03:31:02Z |
| format | Article |
| id | ump-38683 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:31:02Z |
| publishDate | 2023 |
| publisher | Science and Information Organization |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 |
| 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 |
| title | 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_short | Software effort estimation using machine learning technique |
| title_sort | software effort estimation using machine learning technique |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/38683/ http://umpir.ump.edu.my/id/eprint/38683/ http://umpir.ump.edu.my/id/eprint/38683/ 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 |