Deep Learning for Software Defect Prediction: A Global Research Landscape

Current research on software defect prediction using deep learning is being conducted both domestically and internationally. In China, researchers have investigated the use of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for defect prediction. They have also explored the use of ensemble models and transfer learning to improve the accuracy of predictions. In addition, researchers in China have focused on developing hybrid models that combine deep learning with traditional machine learning algorithms.

Internationally, researchers have also explored the use of deep learning for defect prediction. They have investigated the use of different types of neural networks, such as recurrent neural networks (RNNs), CNNs, and autoencoders. They have also explored the use of feature engineering and transfer learning to improve the accuracy of predictions.

Overall, both domestic and international researchers are actively investigating the use of deep learning for software defect prediction. They are exploring different types of neural networks and techniques to improve prediction accuracy.