A Systematic Study of LLM-Based Code Translation from Multiple Perspectives

Abstract

​Large Language Models (LLMs) have demonstrated powerful capabilities in code analysis, exhibiting a deep understanding of code semantics and functionality. Programming code lies at the heart of software development, and the automation and intelligent generation of code can effectively shorten development cycles and reduce labor costs. Current research on code transformation using Large Language Models is gradually emerging. However, these works vary in research perspective, object, and goal, making it difficult to comprehensively evaluate the advantages and characteristics of Large Language Models in code translation tasks. Moreover, existing code translation primarily focuses on simple code translation for explicit tasks and remains incomplete for code translation of complex software systems.Therefore, this paper analyzes the inherent characteristics of Large Language Models in code translation based on their working mechanisms and characteristics. It comprehensively investigates research on code-intelligent generation using Large Language Models over the past two years, particularly examining effectiveness for complex generation tasks and relevant technologies from both task-oriented and technical perspectives. During this process, the impact of prompt engineering methods in code translation is specifically examined. Through systematic analysis and research, it has been found that Large Language Model systems like ChatGPT [1] are effective for code translation tasks with clear objectives. However, they still exhibit room for improvement in handling complex tasks, such as the inability to accurately translate code with tightly coupled contextual logic and the failure to generate code for complex software-system translation.

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