Abstract—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.