Application of Random Forest in Predicting Fault-Prone Classes

• Published in: 2008 International Conference on Advanced Computer Theory and Engineering pp. 37 - 43
• Main Authors: Kaur, A., Malhotra, R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2008
• Subjects: Application software; Bagging; fault prediction; Information technology; machine learning; Machine learning algorithms; Open source software; Prediction algorithms; Radio frequency; Random Forest; Software algorithms; software metrics; Software performance; Software quality
• ISBN: 9780769534893; 0769534899
• ISSN: 2154-7491
• DOI: 10.1109/ICACTE.2008.204

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.