Using Document Similarity Methods to create Parallel Datasets for Code Translation

TL;DR

This paper introduces a method to generate noisy parallel datasets for code translation using document similarity, enabling supervised learning for languages lacking parallel corpora, and demonstrates its effectiveness across multiple programming languages.

Contribution

The paper proposes a novel approach to create parallel datasets from monolingual code corpora using document similarity, facilitating supervised code translation without needing curated datasets.

Findings

Models trained on noisy datasets perform comparably to those trained on ground truth.

The method enables creation of parallel datasets for less-studied programming languages.

The approach expands the applicability of supervised code translation techniques.

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.