By Topic

Application of Random Forest in Predicting Fault-Prone Classes

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Arvinder Kaur ; Univ. Sch. of Inf. Technol., Guru Gobind Singh Indraprastha Univ., Delhi, India ; Ruchika Malhotra

There are available metrics for predicting fault prone classes, which may help software organizations for planning and performing testing activities. This may be possible due to proper allocation of resources on fault prone parts of the design and code of the software. Hence, importance and usefulness of such metrics is understandable, but empirical validation of these metrics is always a great challenge. Random forest (RF) algorithm has been successfully applied for solving regression and classification problems in many applications. This paper evaluates the capability of RF algorithm in predicting fault prone software classes using open source software. The results indicate that the prediction performance of random forest is good. However, similar types of studies are required to be carried out in order to establish the acceptability of the RF model.

Published in:

2008 International Conference on Advanced Computer Theory and Engineering

Date of Conference:

20-22 Dec. 2008