Rewriting the Code: A Simple Method for Large Language Model Augmented
  Code Search
Haochen Li Xin Zhou Zhiqi Shen
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.