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%.

通过利用自动化单元测试系统过滤掉无效翻译从而创建一个完全测试的平行语料库，并利用这个过滤后的数据集对非监督模型进行微调，显著减少所产生的噪声，在 Java $	o$ Python 和 Python $	o$ C++ 方向上，与所有先前的最佳方法相比，分别取得了超过 16% 和 24% 的优异成绩，将错误率降低了超过 35%。