LLMs as Code Architects: Meta’s New Approach to Precise Code Transformations

Tools designed for rewriting, refactoring, and optimizing code should prioritize both speed and accuracy. Large language models (LLMs), however, often lack these critical attributes. Despite these limitations, there remains significant potential for leveraging LLMs to enhance code quality.

In a new paper Don’t Transform the Code, Code the Transforms: Towards Precise Code Rewriting using LLMs, a Meta research team proposes a novel chain-of-thought strategy to efficiently generate code transformations using LLMs. Their approach enables LLMs to derive transformations based on a small set of input/output examples.

A code transformation refers to a function that modifies existing code to achieve a desired form, a method applicable to various tasks — from compiler optimizations to legacy code refactoring. Traditional rule-based transformations, while effective, are often complex to design and implement. LLMs present a promising alternative, but their logic can be opaque, lacks guarantees of correctness, and is difficult to debug when errors occur.