Scalable, Validated Code Translation of Entire Projects using Large Language Models

TL;DR

This paper presents a scalable, validated approach for translating entire large codebases using large language models by partitioning code, applying feature mapping, and ensuring semantic correctness, significantly improving translation success rates.

Contribution

The authors introduce a modular translation method with feature mapping and type-compatibility checks, enabling reliable translation of large codebases with LLMs, surpassing previous scalability limitations.

Findings

Successfully translated up to 6,600 lines of Go code to Rust.

Achieved an average of 73% functions validated for I/O equivalence.

Outperformed existing methods in translation success rate.

Abstract

Large language models (LLMs) show promise in code translation due to their ability to generate idiomatic code. However, a significant limitation when using LLMs for code translation is scalability: existing works have shown a drop in translation success rates for code exceeding around 100 lines. We overcome this limitation by developing a modular approach to translation, where we partition the code into small code fragments which can be translated independently and semantically validated (that is, checking I/O equivalence). When this approach is applied naively, we discover that LLMs are unreliable when translating features of the source language that do not have a direct mapping to the target language, and that the LLM often gets stuck in repair loops when attempting to fix errors. To address these issues, we introduce two key concepts: (1) feature mapping, which integrates predefined translation rules with LLM-based translation to guide the LLM in navigating subtle language differences and producing semantically accurate code; and (2) type-compatibility, which facilitates localized checks at the function signature level to detect errors early, thereby narrowing the scope of potential repairs. We apply our approach to translating real-world Go codebases to Rust, demonstrating that we can consistently generate reliable Rust translations for projects up to 6,600 lines of code and 369 functions, with an average of 73% of functions successfully validated for I/O equivalence, considerably higher than any existing work.