文件名称:Software-faults-prediction-using-multiple-classif
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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.
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.
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Software -faults-prediction-using-multiple-classifiers.pdf