Regression and decision tree approaches in predicting the effort in resolving incidents
by Sharon Christa; V. Suma; Uma Mohan
International Journal of Business Information Systems (IJBIS), Vol. 39, No. 3, 2022

Abstract: IT service management plays a key role in software maintenance. Service management offers the customers a platform to raise the incidents that needs to be resolved. This papers is a comprehensive analysis performed on research in the area of production support. Lacunas are identified in different areas of production support services. The necessity of a generalised proactive model that can predict the effort required in closing incident tickets are identified. The paper further presents the scope of integrating machine learning approaches to predict effort in an incident management system of production support. Two different approaches are considered in modelling namely, regression-based and tree-based modelling. In tree-based modelling, basic decision tree and random forest models are used along with multiple linear regression model. In order to build the model, real-time dataset is used. The models are verified using a real-time test dataset. The models being dataset dependent did not generalise and converge well due to which, the possibility of developing other models using different machine learning techniques are discussed.

Online publication date: Thu, 21-Apr-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Information Systems (IJBIS):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com