In this paper, we leverage low-level compiler intermediate representations (IR) to improve code translation. Traditional transpilers rely on syntactic information and handcrafted rules, which limits their applicability and produces unnatural-looking code. Applying neural machine translation (NMT) approaches to code has successfully broadened the set of programs on which one can get a natural-looking translation. However, they treat the code as sequences of text tokens, and still do not differentiate well enough between similar pieces of code which have different semantics in different languages. The consequence is low quality translation, reducing the practicality of NMT, and stressing the need for an approach significantly increasing its accuracy. Here we propose to augment code translation with IRs, specifically LLVM IR, with results on the C++, Java, Rust, and Go languages. Our method improves upon the state of the art for unsupervised code translation, increasing the number of correct translations by 11% on average, and up to 79% for the Java - Rust pair. We extend previous test sets for code translation, by adding hundreds of Go and Rust functions. Additionally, we train models with high performance on the problem of IR decompilation, generating programming source code from IR, and study using IRs as intermediary pivot for translation.

本文提出了一种新的方法，使用低级别的编译器中间表示(IR)来提高代码的翻译质量，不同于使用传统转换方法的手工指定规则，使用神经机器翻译(NMT)方法进行翻译，并将IRs作为中间变量，能够在C ++，Java，Rust和Go语言中获得自然的翻译结果，平均提高正确翻译数11％，在一些语言对中高达79％，同时还将IRs用作翻译的中间枢轴。

使用编译器表示进行代码翻译