Software defect prediction is an important way to make full use of software test resources and improve software performance.
To deal with the problem of shallow machine learning based software defect prediction model which cannot 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 using data augmentation method to enhance the ability of feature expression, and the autoencoder by forest performs
data 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.
Aiswarya-Sanjay/Software-Defect-Prediction-Model-Based-on-Improved-Deep-Forest-and-AutoEncoder-by-Forest
Built a Software Defect Prediction Model using an improved Deep Forest algorithm and Autoencoder by Forest. The original input features are transformed by data augmentation method to enhance the ability of feature extraction, and the autoencoder by forest performs data dimensionality reduction on the features
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