TITLE: Using Document Similarity Methods to create Parallel Datasets for Code Translation 

ABSTRACT: Translating source code from one programming language to another is a
critical, time-consuming task in modernizing legacy applications and codebases.
Recent work in this space has drawn inspiration from the software naturalness
hypothesis by applying natural language processing techniques towards
automating the code translation task. However, due to the paucity of parallel
data in this domain, supervised techniques have only been applied to a limited
set of popular programming languages. To bypass this limitation, unsupervised
neural machine translation techniques have been proposed to learn code
translation using only monolingual corpora. In this work, we propose to use
document similarity methods to create noisy parallel datasets of code, thus
enabling supervised techniques to be applied for automated code translation
without having to rely on the availability or expensive curation of parallel
code datasets. We explore the noise tolerance of models trained on such
automatically-created datasets and show that these models perform comparably to
models trained on ground truth for reasonable levels of noise. Finally, we
exhibit the practical utility of the proposed method by creating parallel
datasets for languages beyond the ones explored in prior work, thus expanding
the set of programming languages for automated code translation.
Translating source code from one programming language to another is a
critical, time-consuming task in modernizing legacy applications and codebases.
Recent work in this space has drawn inspiration from the software naturalness
hypothesis by applying natural language processing techniques towards
automating the code translation task. However, due to the paucity of parallel
data in this domain, supervised techniques have only been applied to a limited
set of popular programming languages. To bypass this limitation, unsupervised
neural machine translation techniques have been proposed to learn code
translation using only monolingual corpora. In this work, we propose to use
document similarity methods to create noisy parallel datasets of code, thus
enabling supervised techniques to be applied for automated code translation
without having to rely on the availability or expensive curation of parallel
code datasets. We explore the noise tolerance of models trained on such
automatically-created datasets and show that these models perform comparably to
models trained on ground truth for reasonable levels of noise. Finally, we
exhibit the practical utility of the proposed method by creating parallel
datasets for languages beyond the ones explored in prior work, thus expanding
the set of programming languages for automated code translation.
Using Document Similarity Methods to create Parallel Datasets for Code Translation
http://arxiv.org/abs/2110.05423v1