带有外部库的项目验证代码翻译
Validated Code Translation for Projects with External Libraries
AI总结：
本文提出一种框架，用于将依赖外部库的Go项目翻译为Rust，通过检索机制和跨语言验证流水线提高翻译和验证的准确性。
摘要：
大型语言模型（LLMs）在程序翻译方面显示出潜力，特别是在将系统代码迁移到内存安全语言如Rust时。然而，现有方法在源程序依赖外部库时遇到困难：LLMs经常虚构不存在的目标API，并未能生成调用启用的导入；此外，当代码操作不透明的库定义类型时，验证语义等价性具有挑战性。我们提出了一个翻译和验证框架，用于将依赖外部库的Go项目翻译成Rust。我们的方法结合（i）一个检索机制，将Go库API映射到Rust API，以及（ii）一个跨语言验证流水线，通过仅从公共库API合成适配器，在存在不透明库类型的情况下建立语言互操作性。我们评估了我们的系统在六个具有非平凡外部依赖的现实Go仓库上的表现。我们的方法通过启用验证翻译来操作不透明的库定义类型，显著提高了编译和等价性成功率（在最依赖性的案例中达到100%；平均约为2倍）。
英文摘要：
Large Language Models (LLMs) have shown promise for program translation, particularly for migrating systems code to memory-safe languages such as Rust. However, existing approaches struggle when source programs depend on external libraries: LLMs frequently hallucinate non-existent target APIs and fail to generate call-enabling imports; moreover, validating semantic equivalence is challenging when the code manipulates opaque, library-defined types. We present a translation and validation framework for translating Go projects with external dependencies to Rust. Our approach combines (i) a retrieval mechanism that maps Go library APIs to Rust APIs, and (ii) a cross-language validation pipeline that establishes language interoperability in the presence of opaque library types by synthesising adapters exclusively from public library APIs, prior to validating I/O equivalence. We evaluate our system on six real-world Go repositories with non-trivial external dependencies. Our approach significantly increases both the compilation and equivalence success rate (up to 100% in the most dependency-heavy case; approx. 2x on average) by enabling validated translation that manipulate opaque, library-defined types.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
