A survey on machine learning techniques used for software quality prediction
by Saumendra Pattnaik; Binod Kumar Pattanayak
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 8, No. 1/2, 2016

Abstract: In the present software development scenario, software quality prediction has become significantly important for successful implementation of the software in real world application and enhances the longevity of its functionality. Moreover, early identification of anticipated fault prone software modules in the process of development of software is crucial in saving efforts involved in this process. Machine learning techniques are considered to be the most appropriate techniques for software quality prediction and a large spectrum of research work has been conducted in this direction by several authors. In this paper, we conduct an extensive survey on various machine learning techniques like fuzzy logic, neural network, and Bayesian model, etc. used for software quality prediction along with an analytical justification for each of the proposed solutions.

Online publication date: Thu, 27-Oct-2016

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 Reasoning-based Intelligent Systems (IJRIS):
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