  With little to no parallel data available for programming languages,
unsupervised methods are well-suited to source code translation. However, the
majority of unsupervised machine translation approaches rely on
back-translation, a method developed in the context of natural language
translation and one that inherently involves training on noisy inputs.
Unfortunately, source code is highly sensitive to small changes; a single token
can result in compilation failures or erroneous programs, unlike natural
languages where small inaccuracies may not change the meaning of a sentence. To
address this issue, we propose to leverage an automated unit-testing system to
filter out invalid translations, thereby creating a fully tested parallel
corpus. We found that fine-tuning an unsupervised model with this filtered data
set significantly reduces the noise in the translations so-generated,
comfortably outperforming the state-of-the-art for all language pairs studied.
In particular, for Java $\to$ Python and Python $\to$ C++ we outperform the
best previous methods by more than 16% and 24% respectively, reducing the error
rate by more than 35%.
