Exploring Data Augmentation for Code Generation Tasks
This work addresses data scarcity in code generation for developers and researchers, but it is incremental as it adapts existing augmentation methods to a specific domain.
The paper tackled the problem of limited data for downstream code generation tasks by proposing and adapting data augmentation methods, resulting in improvements of up to 6.9% for code translation and 7.5% for code summarization.
Advances in natural language processing, such as transfer learning from pre-trained language models, have impacted how models are trained for programming language tasks too. Previous research primarily explored code pre-training and expanded it through multi-modality and multi-tasking, yet the data for downstream tasks remain modest in size. Focusing on data utilization for downstream tasks, we propose and adapt augmentation methods that yield consistent improvements in code translation and summarization by up to 6.9% and 7.5% respectively. Further analysis suggests that our methods work orthogonally and show benefits in output code style and numeric consistency. We also discuss test data imperfections.