Software Project Management Using Machine Learning Technique—A Review
Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learni...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/11/5183 |
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| author | Mohammed Najah Mahdi Mohd Hazli Mohamed Zabil Abdul Rahim Ahmad Roslan Ismail Yunus Yusoff Lim Kok Cheng Muhammad Sufyian Bin Mohd Azmi Hayder Natiq Hushalini Happala Naidu |
| author_facet | Mohammed Najah Mahdi Mohd Hazli Mohamed Zabil Abdul Rahim Ahmad Roslan Ismail Yunus Yusoff Lim Kok Cheng Muhammad Sufyian Bin Mohd Azmi Hayder Natiq Hushalini Happala Naidu |
| author_sort | Mohammed Najah Mahdi |
| collection | DOAJ |
| description | Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy. |
| first_indexed | 2024-03-10T10:46:09Z |
| format | Article |
| id | doaj.art-ac8ec6e10b4e4eae8a935d03427e7859 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:46:09Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ac8ec6e10b4e4eae8a935d03427e78592023-11-21T22:36:36ZengMDPI AGApplied Sciences2076-34172021-06-011111518310.3390/app11115183Software Project Management Using Machine Learning Technique—A ReviewMohammed Najah Mahdi0Mohd Hazli Mohamed Zabil1Abdul Rahim Ahmad2Roslan Ismail3Yunus Yusoff4Lim Kok Cheng5Muhammad Sufyian Bin Mohd Azmi6Hayder Natiq7Hushalini Happala Naidu8Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Kajang 43000, MalaysiaCollege of Computing and Informatics (CCI), Universiti Tenaga Nasional, Kajang 43000, MalaysiaCollege of Computing and Informatics (CCI), Universiti Tenaga Nasional, Kajang 43000, MalaysiaCollege of Computing and Informatics (CCI), Universiti Tenaga Nasional, Kajang 43000, MalaysiaCollege of Computing and Informatics (CCI), Universiti Tenaga Nasional, Kajang 43000, MalaysiaCollege of Computing and Informatics (CCI), Universiti Tenaga Nasional, Kajang 43000, MalaysiaCollege of Computing and Informatics (CCI), Universiti Tenaga Nasional, Kajang 43000, MalaysiaDepartment of Computer Technology, Information Technology Collage, Imam Ja’afar Al-Sadiq University, Baghdad 10064, IraqUniten R&D Sdn Bhd, Universiti Tenaga Nasional, Kajang 43000, MalaysiaProject management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.https://www.mdpi.com/2076-3417/11/11/5183machine learning techniquesoftware project estimationsoftware estimationsoftware project managementproject risk assessment |
| spellingShingle | Mohammed Najah Mahdi Mohd Hazli Mohamed Zabil Abdul Rahim Ahmad Roslan Ismail Yunus Yusoff Lim Kok Cheng Muhammad Sufyian Bin Mohd Azmi Hayder Natiq Hushalini Happala Naidu Software Project Management Using Machine Learning Technique—A Review Applied Sciences machine learning technique software project estimation software estimation software project management project risk assessment |
| title | Software Project Management Using Machine Learning Technique—A Review |
| title_full | Software Project Management Using Machine Learning Technique—A Review |
| title_fullStr | Software Project Management Using Machine Learning Technique—A Review |
| title_full_unstemmed | Software Project Management Using Machine Learning Technique—A Review |
| title_short | Software Project Management Using Machine Learning Technique—A Review |
| title_sort | software project management using machine learning technique a review |
| topic | machine learning technique software project estimation software estimation software project management project risk assessment |
| url | https://www.mdpi.com/2076-3417/11/11/5183 |
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