  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.
