FT2Ra: A Fine-Tuning-Inspired Approach to Retrieval-Augmented Code Completion

The core message of this paper is to introduce a novel retrieval-augmented method called FT2Ra, which aims to mimic the effects of genuine fine-tuning without the need for actual fine-tuning. FT2Ra is designed to effectively leverage the Δlogits information from retrieved neighbors to enhance the predictions of pre-trained code models.