Training Deep Code Comment Generation Models via Data Augmentation

With the development of deep neural networks (DNNs) and the publicly available source code repositories, deep code comment generation models have demonstrated reasonable performance on test datasets. However, it has been confirmed in computer vision (CV) and natural language processing (NLP) that DNNs are vulnerable to adversarial examples. In this paper, we investigate how to maintain the performance of the models against these perturbed samples. We propose a simple, but effective, method to improve the robustness by training the model via data augmentation. We conduct experiments to evaluate our approach on two mainstream sequence-sequence (seq2seq) architectures which are based on the LSTM and the Transformer with a large-scale publicly available dataset. The experimental results demonstrate that our method can efficiently improve the capability of different models to defend the perturbed samples.