Software Fault-proneness Prediction using Random Forest

Many metric-based classification models have been developed and applied to software fault-proneness prediction. This paper presents a novel prediction model using Random Forest classifier. Random Forest (RF) can be a promising candidate for software quality prediction because it is one of the most accurate classification algorithms available and has strengths in noise handling and efficient running on large data sets. The RF model is constructed and the attribute selection process of the input data is performed before the model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I and Type II error rates, and compared with well-known prediction models, MultiLayer Perceptron (MLP) neural network model and Support Vector Machine (SVM) model. The results show that the RF model significantly outperforms the SVM model and slightly outperforms the MLP model.