Syzygy: Dual Code-Test C to (safe) Rust Translation using LLMs and Dynamic Analysis (2412.14234v2)

Abstract: Despite extensive usage in high-performance, low-level systems programming applications, C is susceptible to vulnerabilities due to manual memory management and unsafe pointer operations. Rust, a modern systems programming language, offers a compelling alternative. Its unique ownership model and type system ensure memory safety without sacrificing performance. In this paper, we present Syzygy, an automated approach to translate C to safe Rust. Our technique uses a synergistic combination of LLM-driven code and test translation guided by dynamic-analysis-generated execution information. This paired translation runs incrementally in a loop over the program in dependency order of the code elements while maintaining per-step correctness. Our approach exposes novel insights on combining the strengths of LLMs and dynamic analysis in the context of scaling and combining code generation with testing. We apply our approach to successfully translate Zopfli, a high-performance compression library with ~3000 lines of code and 98 functions. We validate the translation by testing equivalence with the source C program on a set of inputs. To our knowledge, this is the largest automated and test-validated C to safe Rust code translation achieved so far.

• The paper introduces a modular algorithmic framework that translates C code into safe and compilable Rust using LLMs and iterative refinement.
• It employs dynamic sampling and candidate filtering to validate translations through execution tests, ensuring both syntactic and semantic accuracy.
• The approach enhances inter-language operability, enabling legacy C codebases to leverage Rust's memory safety and concurrency benefits.

An Algorithmic Approach to C to Rust Code Translation

This paper provides an intricate algorithmic framework focused on the translation of C code to Rust using a pipeline that leverages LLMs. The methods presented here address a critical need for seamless inter-language operability and robustness, driven by the ever-increasing demand for safer and more concurrent programming languages like Rust.

Overview of the Translation Pipeline

The paper introduces a modular approach, encapsulated through several core functions: TranslateUnit, Pipeline, CodeGen, TranslateArgs, and EqTest. The TranslateUnit function initiates the process, discerning whether the given C code unit is a function that requires translation or a non-function unit that can be directly transmuted into Rust using a helper CodeGen.

Upon identifying a function, the Pipeline function is invoked. This function forms the crux of the translation process, iteratively attempting to convert C functions into compilable Rust equivalents while ensuring that generated code adheres to Rust's stringent safety and performative guarantees. The pipeline iterates over multiple candidate translations generated by the CodeGen method, filtering those that successfully compile. It further assesses the validity of these translations by executing them in controlled settings, designed by TranslateArgs and EqTest functions, ensuring equivalence in functionality between the source C code and the translated Rust code.

Core Algorithmic Contributions

1. Dynamic Sampling with LLMs: The CodeGen, TranslateArgs, and EqTest functions utilize the predictive capabilities of LLMs to generate candidate Rust code, argument translators, and equivalence tests, respectively. These components are sampled based on prior knowledge of existing translated code (C and Rust) and validated through execution tests. This underscores the iterative nature of the framework, constantly learning and refining through feedback and execution results.
2. Iterative Compilable Optimization: By focusing on compilability within the Pipeline, the algorithm incrementally builds upon prior successful translation units. This approach ensures robustness, potentially reducing translation errors due to direct language incongruities.
3. Translation Verification: The EqTest and associated methodologies ensure that the translated code is not only syntactically correct but also semantically equivalent to its original version. This alignment is paramount in maintaining functional parity across translations.

Practical and Theoretical Implications

The pipeline presents significant practical implications, enabling developers and organizations to harness the benefits of Rust's memory safety and concurrency without manually rewriting extensive C codebases. This can be critical in large-scale systems where refactoring is resource-intensive or in legacy systems where stability must be preserved during the transition.

Theoretically, the paper enriches current understanding of LLMs as effective tools in automated code generation and language translation. The recursive feedback loop formed by sampling, compiling, and testing demonstrates an advanced application of machine learning to static programming languages, suggesting further exploration into hybrid models combining predictive capabilities with rule-based systems.

Future Developments

Future research could explore expansion to other programming languages and integration with continuous integration pipelines to automate the validation process in active development environments. Additionally, as LLMs evolve, enhancing the sampling efficiency and improving the model’s understanding of complex code semantics will further refine translation precision, thereby reducing manual intervention and post-translation verification needs.

The algorithmic framework proposed in this paper positions itself as a vital tool in the translation landscape, leveraging both advanced machine learning techniques and classical compilation strategies to support smooth cross-language interoperability.