A Pilot Study on LLM-Based Agentic Translation from Android to iOS: Pitfalls and Insights

• URL: http://arxiv.org/abs/2507.16037v1
• Date: Mon, 21 Jul 2025 20:11:01 GMT
• Title: A Pilot Study on LLM-Based Agentic Translation from Android to iOS: Pitfalls and Insights
• Abstract summary: Large Language Models (LLMs) were recently leveraged to enhance code translation at different granularities.<n>This study aims to fill this gap by evaluating LLM-based agentic approaches for mobile application translation.<n>We developed a chain of agents that account for dependencies, specifications, program structure, and program control flow.
• Score: 27.632954643254884
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• An Extensive Empirical Study on Code Translation Technique [3.398993044674732]
  We compare representative code translation techniques across methodological paradigms and translation granularities.<n>Our analysis considers executable correctness, code similarity, translation direction, translation, and failure patterns.<n>These findings provide empirical evidence and practical guidance for developing more robust, type-aware, structure-aware, and context-aware code translation techniques.
  arXiv  Detail & Related papers  (2026-08-21T06:41:04Z)
• Efficient Multilingual Reasoning Transfer via Progressive Code-Switching [51.49388869532955]
  Transfer approaches typically rely on distilled target-language reasoning traces from stronger LRMs or online supervision from external judge models.<n>We propose PCS (Progressive Code-Switching), a more efficient transfer framework that requires only lightweight translation.<n> PCS substantially narrows the performance gap between target-language and English reasoning, yielding more language-consistent reasoning.
  arXiv  Detail & Related papers  (2026-07-01T06:13:16Z)
• Finding the Translation Switch: Discovering and Exploiting the Task-Initiation Features in LLMs [69.28193153685893]
  Large Language Models (LLMs) frequently exhibit strong translation abilities, even without task-specific fine-tuning.<n>To demystify this process, we leverage Sparse Autoencoders (SAEs) and introduce a novel framework for identifying task-specific features.<n>Our work not only decodes a core component of the translation mechanism in LLMs but also provides a blueprint for using internal model mechanism to create more robust and efficient models.
  arXiv  Detail & Related papers  (2026-01-16T06:29:07Z)
• Function-to-Style Guidance of LLMs for Code Translation [59.487054943812836]
  We propose F2STrans, a function-to-style guiding paradigm designed to improve the performance of large language models in code translation.<n>Our approach comprises two key stages: (1) Functional learning, which optimize translation correctness using high-quality source-target code pairs.<n>We introduce a novel code translation benchmark that includes up-to-date source code, extensive test cases, and manually annotated ground-truth translations.
  arXiv  Detail & Related papers  (2025-07-15T08:25:02Z)
• Beyond Syntax: Action Semantics Learning for App Agents [60.56331102288794]
  Action Semantics Learning (ASL) is a learning framework where the learning objective is capturing the semantics of the ground truth actions.<n>ASL significantly improves the accuracy and generalisation of App agents over existing methods.
  arXiv  Detail & Related papers  (2025-06-21T12:08:19Z)
• Collaboration is all you need: LLM Assisted Safe Code Translation [4.3764649156831235]
  UniTranslator is a framework that re-imagines code translation as a collaborative endeavor among multiple, compact LLMs.<n>By orchestrating the interaction of specialized agents, UniTranslator achieves a level of accuracy and efficiency that rivals larger, monolithic models.
  arXiv  Detail & Related papers  (2025-03-14T09:42:07Z)
• Exploring Large Language Models for Translating Romanian Computational Problems into English [0.0]
  This study shows that robust large language models (LLMs) can maintain or even enhance their performance in translating less common languages when given well-structured prompts.<n>We evaluate several translation methods across multiple LLMs, including OpenRoLLM, Llama 3.1 8B, Llama 3.2 3B and GPT-4o.
  arXiv  Detail & Related papers  (2025-01-09T22:17:44Z)
• Training of Scaffolded Language Models with Language Supervision: A Survey [62.59629932720519]
  This survey organizes the literature on the design and optimization of emerging structures around post-trained LMs.<n>We refer to this overarching structure as scaffolded LMs and focus on LMs that are integrated into multi-step processes with tools.
  arXiv  Detail & Related papers  (2024-10-21T18:06:25Z)
• TRANSAGENT: An LLM-Based Multi-Agent System for Code Translation [16.46292795782835]
  Code translation is crucial for software migration, system ablation, and cross-platform development. Traditional rule-based methods rely on manually-written rules, which can be time-consuming and often result in less readable code. More recently, the advance of Large Language Models (LLMs) further boosts learning-based code translation. We propose a novel multi-agent system TRANSAGENT, which enhances LLM-based code translation by fixing the syntax errors and semantic errors.
  arXiv  Detail & Related papers  (2024-09-30T02:53:03Z)
• The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights [108.40766216456413]
  We propose a question alignment framework to bridge the gap between large language models' English and non-English performance. Experiment results show it can boost multilingual performance across diverse reasoning scenarios, model families, and sizes. We analyze representation space, generated response and data scales, and reveal how question translation training strengthens language alignment within LLMs.
  arXiv  Detail & Related papers  (2024-05-02T14:49:50Z)
• Exploring and Unleashing the Power of Large Language Models in Automated Code Translation [40.25727029618665]
  This paper investigates diverse LLMs and learning-based transpilers for automated code translation tasks. UniTrans is a Unified code Translation framework, applicable to various LLMs. Three recent LLMs of diverse sizes are tested with UniTrans, and all achieve substantial improvements.
  arXiv  Detail & Related papers  (2024-04-23T00:49:46Z)
• Building Accurate Translation-Tailored LLMs with Language Aware Instruction Tuning [57.323716555996114]
  Off-target translation remains an unsolved problem, especially for low-resource languages. Recent works have either designed advanced prompting strategies to highlight the functionality of translation instructions or exploited the in-context learning ability of LLMs. In this work, we design a two-stage fine-tuning algorithm to improve the instruction-following ability (especially the translation direction) of LLMs.
  arXiv  Detail & Related papers  (2024-03-21T13:47:40Z)
• Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directions [0.0]
  Generative large language models (LLMs) have demonstrated exceptional proficiency in various natural language processing (NLP) tasks. We conducted an investigation into two popular prompting methods and their combination, focusing on cross-language combinations of Persian, English, and Russian.
  arXiv  Detail & Related papers  (2024-01-16T15:16:34Z)
• DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models [76.79929883963275]
  DIALIGHT is a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems. It features a secure, user-friendly web interface for fine-grained human evaluation at both local utterance level and global dialogue level. Our evaluations reveal that while PLM fine-tuning leads to higher accuracy and coherence, LLM-based systems excel in producing diverse and likeable responses.
  arXiv  Detail & Related papers  (2024-01-04T11:27:48Z)
• Cross-lingual Transferring of Pre-trained Contextualized Language Models [73.97131976850424]
  We propose a novel cross-lingual model transferring framework for PrLMs: TreLM. To handle the symbol order and sequence length differences between languages, we propose an intermediate TRILayer" structure. We show the proposed framework significantly outperforms language models trained from scratch with limited data in both performance and efficiency.
  arXiv  Detail & Related papers  (2021-07-27T06:51:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.