Automated Snippet-Alignment Data Augmentation for Code Translation

• URL: http://arxiv.org/abs/2510.15004v1
• Date: Thu, 16 Oct 2025 02:30:24 GMT
• Title: Automated Snippet-Alignment Data Augmentation for Code Translation
• Authors: Zhiming Zhang, Qingfu Zhu, Xianzhen Luo, Yixuan Wang, Bohan Li, Wanxiang Che,
• Abstract summary: parallel corpora can be categorized into program-alignment (PA) and snippet-alignment (SA) data.<n>We propose a data augmentation method that leverages LLMs to generate SA data automatically.<n>Experiments on TransCoder-test demonstrate that our augmented SA data combined with the two-stage training approach yields consistent improvements.
• Score: 51.59756295898321
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Code translation aims to translate the code from its source language to the target language and is used in various software development scenarios. Recent developments in Large Language Models (LLMs) have showcased their capabilities in code translation, and parallel corpora play a crucial role in training models for code translation. Parallel corpora can be categorized into program-alignment (PA) and snippet-alignment (SA) data. Although PA data has complete context and is suitable for semantic alignment learning, it may not provide adequate fine-grained training signals due to its extended length, while the brevity of SA data enables more fine-grained alignment learning. Due to limited parallel corpora, researchers explore several augmentation methods for code translation. Previous studies mainly focus on augmenting PA data. In this paper, we propose a data augmentation method that leverages LLMs to generate SA data automatically. To fully leverage both PA data and SA data, we explore a simple yet effective two-stage training strategy, which consistently enhances model performance compared to fine-tuning solely on PA data. Experiments on TransCoder-test demonstrate that our augmented SA data combined with the two-stage training approach yields consistent improvements over the baseline, achieving a maximum gain of 3.78% on pass@k.

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