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One major challenge of translating code between programming languages is thatparallel training data is often limited. To overcome this challenge, we presenttwo data augmentation techniques, one that builds comparable corpora (i.e.,code pairs with similar functionality), and another that augments existingparallel data with multiple reference translations. Specifically, we build andanalyze multiple types of comparable corpora, including programs generated fromnatural language documentation using a code generation model. Furthermore, toreduce overfitting to a single reference translation, we automatically generateadditional translation references for available parallel data and filter thetranslations by unit tests, which increases variation in target translations.Experiments show that our data augmentation techniques significantly improveCodeT5 for translation between Java, Python, and C++ by an average of 7.5%Computational Accuracy (CA@1), which verifies the correctness of translationsby execution. The code is available at https://github.com/Veronicium/CMTrans.

Data Augmentation for Code Translation with Comparable Corpora and Multiple References