Version 1
: Received: 13 June 2023 / Approved: 13 June 2023 / Online: 13 June 2023 (08:19:19 CEST)
How to cite:
Ranesh, M. A.; Samuel, S. J. Machine Learning Model for Improvement of Project Team Members Skillset through Software Governance. Preprints2023, 2023060894. https://doi.org/10.20944/preprints202306.0894.v1
Ranesh, M. A.; Samuel, S. J. Machine Learning Model for Improvement of Project Team Members Skillset through Software Governance. Preprints 2023, 2023060894. https://doi.org/10.20944/preprints202306.0894.v1
Ranesh, M. A.; Samuel, S. J. Machine Learning Model for Improvement of Project Team Members Skillset through Software Governance. Preprints2023, 2023060894. https://doi.org/10.20944/preprints202306.0894.v1
APA Style
Ranesh, M. A., & Samuel, S. J. (2023). Machine Learning Model for Improvement of Project Team Members Skillset through Software Governance. Preprints. https://doi.org/10.20944/preprints202306.0894.v1
Chicago/Turabian Style
Ranesh, M. A. and S. Justin Samuel. 2023 "Machine Learning Model for Improvement of Project Team Members Skillset through Software Governance" Preprints. https://doi.org/10.20944/preprints202306.0894.v1
Abstract
Software governance is a management structure that guides projects in terms of their accountability and responsibility. Prime motivation of this approach is to improve the skillset of the team members through software governance policies and increase the overall success rate of the software projects. The scope of skill development is across the pillars of governance, such as structure, people, and information. Primary focus of this paper is on the skillset development of the project team members through educational policies in software governance. As part of the governance process, educational policies are defined for the skillset development of project team members. The JIRA dataset was used to determine the skillset development of the team members. Machine learning techniques, such as J48, Random Forest, Decision Table, Logistics, and Naïve Bayes, were used in the JIRA dataset. These machine learning techniques were processed using WEKA open-source software. Based on these results, it was concluded that the J48 algorithm can be applied to multiple projects/programs to monitor and track the skill development process. Machine learning model such as J48 is required to use this model at an organizational level. The skillset development of project team members should be aligned with IT governance and educational policies. Overall upskilling and reskilling strategies are provided to demonstrate the impact of skillset development through software governance.
Keywords
Educational policies; Learning and development; Machine Learning Techniques; Skillset; IT Governance; Team Members
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
