  Pre-trained language models have achieved promising success in code retrieval
tasks, where a natural language documentation query is given to find the most
relevant existing code snippet. However, existing models focus only on
optimizing the documentation code pairs by embedding them into latent space,
without the association of external knowledge. In this paper, we propose a
generation-augmented query expansion framework. Inspired by the human retrieval
process - sketching an answer before searching, in this work, we utilize the
powerful code generation model to benefit the code retrieval task.
Specifically, we demonstrate that rather than merely retrieving the target code
snippet according to the documentation query, it would be helpful to augment
the documentation query with its generation counterpart - generated code
snippets from the code generation model. To the best of our knowledge, this is
the first attempt that leverages the code generation model to enhance the code
retrieval task. We achieve new state-of-the-art results on the CodeSearchNet
benchmark and surpass the baselines significantly.
