Software Project Management Using Machine Learning Technique—A Review

Abstract

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.

Machine Learning Approach for Targeting and Recommending a Product for Project Management

“…(3) PART-3: Review data analysis to know more about the dataset; The proposed approach is totally different and more advanced (based on CCI dataset only) than the model mentioned in the literature [56][57][58]. In [56], the author represents a state-of-the-art related to software project management using ML techniques based on Web Science, Science Directs, and IEEE Explore. A total of 111 research papers have been categorized into four categories.…”

“…In [58], a statistical and ML approach based on the two Bass models is developed for forecasting the demand. The main advancement of the proposed approach is that these three ML approaches [56][57][58] are not based on CCI datasets.…”

Related Literature Review 5D Model for Project and Operation/Maintenance Remote Monitoring of Equipment and Piping System

“…As a result, project managers have challenges sustaining a software project's sustainability and increasing efficiency. Data gathered feature extraction, model creation, and analysis steps in the software project management (SPM) estimate process [59].…”

Una Aproximación Dinámica del Desempeño en Proyectos de Desarrollo de Software

“…The problems corresponding to this category are the following: Failure in risk management (Stepanek, 2005;Martinez and Arango, 2017;Liu and Liu, 2015;Almomani et al, 2015;Al et al, 2019;Mahdi et al, 2021), inadequate control (Özkaya et al, 2011;Wang et al, 2012;Martinez and Arango, 2017), and lack of security and confidentiality (Ahonen and Junttila, 2003;Haile and Altmann, 2016).…”