  Code search is to search reusable code snippets from source code corpus based
on natural languages queries. Deep learning-based methods of code search have
shown promising results. However, previous methods focus on retrieval accuracy
but lacked attention to the efficiency of the retrieval process. We propose a
novel method CoSHC to accelerate code search with deep hashing and code
classification, aiming to perform an efficient code search without sacrificing
too much accuracy. To evaluate the effectiveness of CoSHC, we apply our method
to five code search models. Extensive experimental results indicate that
compared with previous code search baselines, CoSHC can save more than 90% of
retrieval time meanwhile preserving at least 99% of retrieval accuracy.
