Data Augmentation for Code Translation with Comparable Corpora and Multiple References
This addresses the data scarcity issue for code translation tasks, providing incremental improvements in accuracy for translating between Java, Python, and C++.
The paper tackles the limited parallel training data problem in code translation by introducing two data augmentation techniques: building comparable corpora and generating multiple reference translations, which improve CodeT5's translation accuracy by an average of 7.5% Computational Accuracy (CA@1).
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.