According to a report from Zhanchangzhijia, research indicates that code data augmentation techniques hold significant potential in deep learning. This technology can simulate the context of code snippets by training on extensive corpora of source code and has already demonstrated exceptional performance in various downstream tasks related to source code. Code data augmentation methods are primarily categorized into rule-based techniques, model-based techniques, and example interpolation techniques, each with its own characteristics and suitable scenarios. This technology can enhance the performance and robustness of models, playing a crucial role in improving model robustness and in low-resource domains. However, despite some encouraging results, further research and exploration are still needed for code data augmentation techniques within the field of deep learning.