用英文告诉我深度学习进行软件缺陷预测的国内外研究现状。最后给我翻译一下

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.

翻译：目前，国内外的研究人员都在积极探索使用深度学习进行软件缺陷预测。在中国，研究人员已经研究了使用卷积神经网络（CNN）和长短期记忆（LSTM）网络进行缺陷预测的方法。他们还探索了集成模型和迁移学习等方法以提高预测准确性。此外，中国的研究人员还专注于开发将深度学习与传统机器学习算法相结合的混合模型。

在国际上，研究人员也在探索使用深度学习进行缺陷预测。他们研究了不同类型的神经网络，如循环神经网络（RNN）、CNN和自编码器等。他们还探索了特征工程和迁移学习等技术来提高预测准确性。

总体而言，国内外的研究人员都在积极探索使用深度学习进行软件缺陷预测。他们正在探索不同类型的神经网络和技术以提高预测准确性。