Pre-trained code models lead the era of code intelligence. Many models have
been designed with impressive performance recently. However, one important
problem, data augmentation for code data that automatically helps developers
prepare training data lacks study in the field of code learning. In this paper,
we introduce a general data augmentation framework, GenCode, to enhance the
training of code understanding models. GenCode follows a
generation-and-selection paradigm to prepare useful training codes.
Specifically, it uses code transformation techniques to generate new code
candidates first and then selects important ones as the training data by
importance metrics. To evaluate the effectiveness of GenCode with a general
importance metric -- loss value, we conduct experiments on four code
understanding tasks (e.g., code clone detection) and three pre-trained code
models (e.g., CodeT5). Compared to the state-of-the-art (SOTA) code
augmentation method, MixCode, GenCode produces code models with 2.92% higher
accuracy and 4.90% robustness on average.

通过引入通用的数据增强框架 GenCode，该论文提出了一种用于增强代码理解模型训练的方法，通过代码转换技术生成新的代码候选项，并利用重要性度量选择重要的代码作为训练数据，实验证明 GenCode 相比 MixCode 增强方法，在平均准确率上提高了 2.92% 并且在鲁棒性方面提高了 4.90%。