The rapid advancement of mobile applications has led to a significant demand for cross-platform compatibility, particularly between the Android and iOS platforms. Traditional approaches to mobile application translation often rely on manual intervention or rule-based systems, which are labor-intensive and time-consuming. While recent advancements in machine learning have introduced automated methods, they often lack contextual understanding and adaptability, resulting in suboptimal translations. Large Language Models (LLMs) were recently leveraged to enhance code translation at different granularities, including the method, class, and repository levels. Researchers have investigated common errors, limitations, and potential strategies to improve these tasks. However, LLM-based application translation across different platforms, such as migrating mobile applications between Android and iOS or adapting software across diverse frameworks, remains underexplored. Understanding the performance, strengths, and limitations of LLMs in cross-platform application translation is critical for advancing software engineering automation. This study aims to fill this gap by evaluating LLM-based agentic approaches for mobile application translation, identifying key failure points, and proposing guidelines to improve translation performance. We developed a chain of agents that account for dependencies, specifications, program structure, and program control flow when translating applications from Android to iOS. To evaluate the performance, we manually examined the translated code for syntactic correctness, semantic accuracy, and functional completeness. For translation failures, we further conducted a detailed root cause analysis to understand the underlying limitations of the agentic translation process and identify opportunities for improvement.

翻译：移动应用的快速发展催生了跨平台兼容性的巨大需求，尤其在Android与iOS平台之间。传统的移动应用翻译方法通常依赖人工干预或基于规则的系统，这些方法劳动密集且耗时。尽管机器学习的最新进展引入了自动化方法，但它们往往缺乏上下文理解与适应性，导致翻译效果欠佳。近期，大型语言模型（LLMs）已被用于增强不同粒度（包括方法、类和仓库级别）的代码翻译。研究者已针对常见错误、局限性及改进策略展开探讨。然而，基于LLM的跨平台应用翻译——例如在Android与iOS间迁移移动应用，或在不同框架间适配软件——仍处于探索不足的状态。理解LLMs在跨平台应用翻译中的性能、优势与局限，对于推进软件工程自动化至关重要。本研究旨在填补这一空白，通过评估基于LLM的代理式移动应用翻译方法，识别关键失败点，并提出提升翻译性能的指导原则。我们开发了一套代理链，在从Android向iOS翻译应用时，能够处理依赖关系、规范要求、程序结构及程序控制流。为评估性能，我们人工检查了翻译后代码的句法正确性、语义准确性和功能完整性。针对翻译失败案例，我们进一步开展了详细的根本原因分析，以理解代理式翻译过程的内在局限性，并识别改进机会。