arXiv cs.AI· Ergun Bi\c{c}ici, Mehmet Kaygusuz, Fatih Turan G\"undo\u{g}du, Emre Filiz, \c{C}a\u{g}atay K{\i}z{\i}lda\u{g}, B\"unyamin Eymen Alag\"oz, Emincan \"Ozcan, Sadi \c{C}elik, Tolunay \"Ozt\"urk, Ege Bulut, Said Aldemir·· 14 小时前AI 评分32
HMOS Migrator：用智能体流水线将应用迁移到 HarmonyOS NEXT 并做产物验证
Cross-Platform Application Migration to HarmonyOS: An Agentic Pipeline with Artifact-Derived Verification
AI 导读
HMOS Migrator 是一套智能体流水线，可读取应用源码生成 ArkTS 工程，并在真机上回放录制交互来验证迁移结果。在六款开源应用的十二次运行中，结构复现完整度达 83.3%–100.0%，但真机行为（4.3%–63.1%）与 UI 保真度（21.1%–63.2%）仍是主要瓶颈。评分仅依赖源应用录制行为与流水线产物，整体得分以每天 +0.45 分稳步提升（R²=0.44，p=0.019）。
正文 · 原文
Authors:Ergun Biçici, Mehmet Kaygusuz, Fatih Turan Gündoğdu, Emre Filiz, Çağatay Kızıldağ, Bünyamin Eymen Alagöz, Emincan Özcan, Sadi Çelik, Tolunay Öztürk, Ege Bulut, Said Aldemir
Abstract:The migration of applications written for a different platforms like Android or iOS to HarmonyOS NEXT requires a complete rewrite in ArkTS. Although LLMs automate the translation of source constructs into ArkTS, syntactic compilation is not enough and functional assurance is a critical open challenge in HarmonyOS NEXT migration. We present HMOS~Migrator, an agentic pipeline that reads an application's source code, generates an ArkTS project, and validates the result by replaying recorded interactions on a real device. We cast the scoring problem as quality estimation without a reference implementation; we score a migration using only the source application's recorded behaviour and the artifacts produced by the pipeline. We define seven weighted metrics, each constructed so that reduced measurement cannot artificially raise the score. Over twelve runs across six open-source applications, the pipeline's structural reproduction is high (completeness \pct{83.3}--\pct{100.0}), while on-device behaviour (\pct{4.3}--\pct{63.1}) and UI fidelity (\pct{21.1}--\pct{63.2}) remain the primary limits. Scores improve steadily over time: $+0.45$~points per day overall ($R^2=0.44$, $p=0.019$) and $+0.47$ over the model-free subset ($R^2=0.39$, $p=0.029$), with corresponding to fit angles of $+8.4^{\circ}$ and $+11.7^{\circ}$ respectively.
| Comments: | 6 pages, 2 figures |
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) |
| ACM classes: | D.2.7; D.2.5; D.2.8; I.2.2 |
| Cite as: | arXiv:2610.33197 [cs.SE] |
| (or arXiv:2610.33197v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33197 arXiv-issued DOI via DataCite |
Submission history
From: Ergun Biçici [view email]
[v1]
Fri, 23 Oct 2026 18:46:17 UTC (14 KB)
来源：arXiv cs.AI · arxiv.org