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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·· 3 小时前AI 评分31

HMOS Migrator:基于智能体流水线与工件验证的跨平台应用迁移至 HarmonyOS 方法

Cross-Platform Application Migration to HarmonyOS: An Agentic Pipeline with Artifact-Derived Verification

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研究者提出 HMOS Migrator,一个将 Android 或 iOS 应用源码迁移为 HarmonyOS NEXT 的 ArkTS 项目的智能体流水线,并通过在真实设备上重放录制交互、仅依据源应用行为与流水线产物的无参考质量估计来评分。

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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

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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