A Survey on Code Representation

Recently, many machine learning models have been proposed to understand and analyze Programming Languages (PLs). While there are some similarities between PLs and Natural Language Processing (NLP), the former one has its own unique challenges. In this survey, we investigate current approaches tackling representation learning of codes and associated downstream tasks that can be solved with them. We present and compare the state-of-the-art models specifically designed for embedding PLs in low-dimensional space, and demonstrate how these embedding methods are related to representation learning approaches in NLP. We also compare benchmark experiments on multiple code-related tasks and evaluate the models for each specific application.