One major challenge of translating code between programming languages is that
parallel training data is often limited. To overcome this challenge, we present
two data augmentation techniques, one that builds comparable corpora (i.e.,
code pairs with similar functionality), and another that augments existing
parallel data with multiple reference translations. Specifically, we build and
analyze multiple types of comparable corpora, including programs generated from
natural language documentation using a code generation model. Furthermore, to
reduce overfitting to a single reference translation, we automatically generate
additional translation references for available parallel data and filter the
translations by unit tests, which increases variation in target translations.
Experiments show that our data augmentation techniques significantly improve
CodeT5 for translation between Java, Python, and C++ by an average of 7.5%
Computational Accuracy (CA@1), which verifies the correctness of translations
by execution. The code is available at this https URL

通过构建可比较的语料库和多重参考翻译，以及使用自动生成的翻译参考和单元测试进行筛选，我们提出了两种数据增强技术来克服不同编程语言之间的代码翻译挑战，并通过平均 7.5% 的计算准确度改进了 CodeT5 的 Java、Python 和 C++ 代码翻译性能。