Structured Code Representations Enable Data-Efficient Adaptation of Code
  Language Models
Mayank Agarwal Yikang Shen Bailin Wang Yoon Kim Jie Chen
Current language models tailored for code tasks often adopt the
pre-training-then-fine-tuning paradigm from natural language processing,
modeling source code as plain text. This approach, however, overlooks the
unambiguous structures inherent in programming languages. In this work, we
explore data-efficient adaptation of pre-trained code models by further
pre-training and fine-tuning them with program structures. Specifically, we
represent programs as parse trees -- also known as concrete syntax trees (CSTs)
-- and adapt pre-trained models on serialized CSTs. Although the models that we
adapt have been pre-trained only on the surface form of programs, we find that
a small amount of continual pre-training and fine-tuning on CSTs without
changing the model architecture yields improvements over the baseline approach
across various code tasks. The improvements are found to be particularly
significant when there are limited training examples, demonstrating the
effectiveness of integrating program structures with plain-text representation
even when working with backbone models that have not been pre-trained with
structures.