Data Augmentation for Code Translation with Comparable Corpora and
  Multiple References
Yiqing Xie Atharva Naik Daniel Fried Carolyn Rose
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 https://github.com/Veronicium/CMTrans.