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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| Main Authors: | , , , , , , , , |
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| Format: | Article |
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MDPI AG
2023
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| Summary: | 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. � 2021 by the author. Licensee MDPI, Basel, Switzerland. |
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