A transcompiler, also known as source-to-source translator, is a system that
converts source code from a high-level programming language (such as C++ or
Python) to another. Transcompilers are primarily used for interoperability, and
to port codebases written in an obsolete or deprecated language (e.g. COBOL,
Python 2) to a modern one. They typically rely on handcrafted rewrite rules,
applied to the source code abstract syntax tree. Unfortunately, the resulting
translations often lack readability, fail to respect the target language
conventions, and require manual modifications in order to work properly. The
overall translation process is timeconsuming and requires expertise in both the
source and target languages, making code-translation projects expensive.
Although neural models significantly outperform their rule-based counterparts
in the context of natural language translation, their applications to
transcompilation have been limited due to the scarcity of parallel data in this
domain. In this paper, we propose to leverage recent approaches in unsupervised
machine translation to train a fully unsupervised neural transcompiler. We
train our model on source code from open source GitHub projects, and show that
it can translate functions between C++, Java, and Python with high accuracy.
Our method relies exclusively on monolingual source code, requires no expertise
in the source or target languages, and can easily be generalized to other
programming languages. We also build and release a test set composed of 852
parallel functions, along with unit tests to check the correctness of
translations. We show that our model outperforms rule-based commercial
baselines by a significant margin.
Speakers: Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, Guillaume Lample