A Systematic Literature Review on Neural Code Translation

TL;DR

This paper systematically reviews recent research on neural code translation, analyzing 57 studies from 2020 to 2025 to identify trends, challenges, and future directions in the field.

Contribution

It provides the first comprehensive review of neural code translation techniques, challenges, and research trends, filling a significant gap in the literature.

Findings

Current research trends identified in neural code translation.

Unresolved challenges in data quality and model generalization.

Potential future research directions outlined.

Abstract

Code translation aims to convert code from one programming language to another automatically. It is motivated by the need for multi-language software development and legacy system migration. In recent years, neural code translation has gained significant attention, driven by rapid advancements in deep learning and large language models. Researchers have proposed various techniques to improve neural code translation quality. However, to the best of our knowledge, no comprehensive systematic literature review has been conducted to summarize the key techniques and challenges in this field. To fill this research gap, we collected 57 primary studies covering the period 2020~2025 on neural code translation. These studies are analyzed from seven key perspectives: task characteristics, data preprocessing, code modeling, model construction, post-processing, evaluation subjects, and evaluation metrics. Our analysis reveals current research trends, identifies unresolved challenges, and shows potential directions for future work. These findings can provide valuable insights for both researchers and practitioners in the field of neural code translation.