In code search, the Generation-Augmented Retrieval (GAR) framework, which
generates exemplar code snippets to augment queries, has emerged as a promising
strategy to address the principal challenge of modality misalignment between
code snippets and natural language queries, particularly with the demonstrated
code generation capabilities of Large Language Models (LLMs). Nevertheless, our
preliminary investigations indicate that the improvements conferred by such an
LLM-augmented framework are somewhat constrained. This limitation could
potentially be ascribed to the fact that the generated codes, albeit
functionally accurate, frequently display a pronounced stylistic deviation from
the ground truth code in the codebase. In this paper, we extend the
foundational GAR framework and propose a simple yet effective method that
additionally Rewrites the Code (ReCo) within the codebase for style
normalization. Experimental results demonstrate that ReCo significantly boosts
retrieval accuracy across sparse (up to 35.7%), zero-shot dense (up to 27.6%),
and fine-tuned dense (up to 23.6%) retrieval settings in diverse search
scenarios. To further elucidate the advantages of ReCo and stimulate research
in code style normalization, we introduce Code Style Similarity, the first
metric tailored to quantify stylistic similarities in code. Notably, our
empirical findings reveal the inadequacy of existing metrics in capturing
stylistic nuances.

通过对代码搜索中的样例代码进行生成和重写，我们提出了一种简单而有效的方法来解决代码风格偏差的问题，并引入了用于量化代码风格相似性的度量指标 Code Style Similarity。实验证明，这种方法在不同的搜索场景中显著提高了检索准确性。

重写代码：大型语言模型增强代码搜索的简单方法

Rewriting the Code: A Simple Method for Large Language Model Augmented  Code Search

We propose Generation-Augmented Retrieval (GAR) for answering open-domain
questions, which augments a query through text generation of heuristically
discovered relevant contexts without external resources as supervision. We
demonstrate that the generated contexts substantially enrich the semantics of
the queries and GAR with sparse representations (BM25) achieves comparable or
better performance than state-of-the-art dense retrieval methods such as DPR.
We show that generating diverse contexts for a query is beneficial as fusing
their results consistently yields better retrieval accuracy. Moreover, as
sparse and dense representations are often complementary, GAR can be easily
combined with DPR to achieve even better performance. GAR achieves
state-of-the-art performance on Natural Questions and TriviaQA datasets under
the extractive QA setup when equipped with an extractive reader, and
consistently outperforms other retrieval methods when the same generative
reader is used.

本文提出了一种 Generation-Augmented Retrieval 方法来回答开放领域的问题，这种方法通过发现相关语境并进行文本生成来增强查询，同时证明所生成的语境大大丰富了查询的语义，使用 BM25 稀疏表示与 DPU 等最先进的稠密检索方法相比，GAR 能够获得同等甚至更好的检索效果。本文还将 G.A.R. 与 DPU 相结合，能够在 Natural Questions 和 TriviaQA 数据集上显着提高提取式 QA 基线的性能，并且在使用相同的生成性阅读器时，GAR 频繁地优于其他检索算法。