Software defect prediction model based on improved deep forest and AutoEncoder by forest

Abstract

Software defect prediction is an important way to make full use of software test resources and improve software performance. To deal with the problem that of the shallow machine learning based software defect prediction model can not deeply mine the software tool data, we propose software defect prediction model based on improved deep forest and autoencoder by forest. Firstly, the original input features are transformed by the data augmentation method to enhance the ability of feature expression, and the autoencoder by forest performs the data of dimensionality reduction on the features. Then, we use the improved deep forest algorithm and autoencoder by forest to build software defect prediction model. The experimental results show that the proposed algorithm has higher performance than the original deep forest (gcForest) algorithm and other existing start-of-art algorithms, and has higher performance and efficiency than other deep learning algorithms.