  Code writing is repetitive and predictable, inspiring us to develop various
code intelligence techniques. This survey focuses on code search, that is, to
retrieve code that matches a given query by effectively capturing the semantic
similarity between the query and code. Deep learning, being able to extract
complex semantics information, has achieved great success in this field.
Recently, various deep learning methods, such as graph neural networks and
pretraining models, have been applied to code search with significant progress.
Deep learning is now the leading paradigm for code search. In this survey, we
provide a comprehensive overview of deep learning-based code search. We review
the existing deep learning-based code search framework which maps query/code to
vectors and measures their similarity. Furthermore, we propose a new taxonomy
to illustrate the state-of-the-art deep learning-based code search in a
three-steps process: query semantics modeling, code semantics modeling, and
matching modeling which involves the deep learning model training. Finally, we
suggest potential avenues for future research in this promising field.
