  Code intelligence leverages machine learning techniques to extract knowledge
from extensive code corpora, with the aim of developing intelligent tools to
improve the quality and productivity of computer programming. Currently, there
is already a thriving research community focusing on code intelligence, with
efforts ranging from software engineering, machine learning, data mining,
natural language processing, and programming languages. In this paper, we
conduct a comprehensive literature review on deep learning for code
intelligence, from the aspects of code representation learning, deep learning
techniques, and application tasks. We also benchmark several state-of-the-art
neural models for code intelligence, and provide an open-source toolkit
tailored for the rapid prototyping of deep-learning-based code intelligence
models. In particular, we inspect the existing code intelligence models under
the basis of code representation learning, and provide a comprehensive overview
to enhance comprehension of the present state of code intelligence.
Furthermore, we publicly release the source code and data resources to provide
the community with a ready-to-use benchmark, which can facilitate the
evaluation and comparison of existing and future code intelligence models
(https://xcodemind.github.io). At last, we also point out several challenging
and promising directions for future research.
