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Software faults prediction using multiple classifiers

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1 Author(s)
Bhekisipho Twala ; Department of Electrical and Electronic Engineering Science, University of Johannesburg, South Africa

In recent years, the use of machine learning algorithms (classifiers) has proven to be of great value in solving a variety of problems in software engineering including software faults prediction. This paper extends the idea of predicting software faults by using an ensemble of classifiers which has been shown to improve classification performance in other research fields. Benchmarking results on two NASA public datasets show all the ensembles achieving higher accuracy rates compared with individual classifiers. In addition, boosting with AR and DT as components of an ensemble is more robust for predicting software faults.

Published in:

Computer Research and Development (ICCRD), 2011 3rd International Conference on  (Volume:4 )

Date of Conference:

11-13 March 2011