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

Generation-Augmented Query Expansion For Code Retrieval