Generation-Augmented Query Expansion For Code Retrieval

Understand

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.