A Survey on Risk Analysis in Information Technology Infrastructure Library (ITIL) Change Management Using Supervised Machine Learning

Authors

  • Srushti Gajjar  U. & PU. Patel Department of Computer Engineering, Chandubhai S. Patel Institute of Technology, CSPIT, Charotar University of Science and Technology, Charusat, Changa, India
  • Mrugendrasinh Rahevar   U. & PU. Patel Department of Computer Engineering, Chandubhai S. Patel Institute of Technology, CSPIT, Charotar University of Science and Technology, Charusat, Changa, India

DOI:

https://doi.org/10.32628/CSEIT206371

Keywords:

ITIL Change Management, Risk Analysis, Support Vector Machine, Naïve Bayes, Logistic Regression, KNN

Abstract

Innovation in IT and technology leads to new developments within the organization. It is important for companies to respond more quickly to the changing trends in order to stay competitive. ITIL change management allows companies to introduce new technologies without interruption or downtime. It follows a standard practice to avoid any unwanted interruptions and involves evaluation, planning and approval of changes. Change Management is all about managing risk for the company and it is linked to the perception of risk that the company has. Risk Analysis is primary component when it comes to any software changes; organizations are concerned about risk management. For better performance by identifying and assessing risk in systematic manner is the aim of the risk management. In ITIL change management risk assessment is a manual process. Automation of risk analysis would have enormous benefits, like reducing the downtime, maximize the productivity and so on. So this paper is mainly on the survey of different supervised machine algorithms of machine learning, like support vector machine, Naive Bayes, logistic regressions.

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Published

2020-06-30

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Section

Research Articles

How to Cite

[1]
Srushti Gajjar, Mrugendrasinh Rahevar , " A Survey on Risk Analysis in Information Technology Infrastructure Library (ITIL) Change Management Using Supervised Machine Learning " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 3, pp.298-303, May-June-2020. Available at doi : https://doi.org/10.32628/CSEIT206371