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新论文 · v1arXiv:2611.11192

Beyond Code Translation: Agentic Android-to-HarmonyOS Migration

Yinghang Ma, Wuyang Dai, Song Wang, Filipe Cogo, Zhen Ming, Jiang

原文摘要

Migrating Android applications to HarmonyOS is an important yet challenging software engineering task. Existing large language model (LLM)-based migration approaches primarily translate code at the method or file level, overlooking project-wide dependencies and workflow coordination. Moreover, ArkTS, the primary language for HarmonyOS, is relatively low-resource, and migrated applications often lack sufficient tests for reliable verification, making end-to-end migration error-prone. We present A2HTrans, an agentic framework for end-to-end Android-to-HarmonyOS application migration. Rather than performing isolated code translation, A2HTrans decomposes applications into functionality-aware migration tasks, prioritizes them through dependency analysis, and orchestrates translation, validation, and iterative repair within a unified execution workflow. To improve migration reliability, A2HTrans employs a tool-augmented verification strategy that combines compiler-based syntax checking with LLM-based semantic validation and automated repair. We evaluate A2HTrans on 14 Android applications spanning diverse sizes and domains using Claude-Sonnet-4.6 as the underlying migration model. Compared with Claude Code, A2HTrans preserves 8.3% more functional completeness, improves behavioral correctness by 8.0%, and reduces migration time by 28.8%. These results demonstrate that structured agentic execution substantially improves the reliability and efficiency of project-level cross-platform software migration.

cs.SE

新论文 · v1arXiv:2610.35616

Hierarchical Skill Evolution for Coding Agent

Jun Gao, Yun Peng, Changhai Zhou, Yuhan Zhu, Xiangyu Zeng, Shiyang Zhang Yuhua Zhou, Luozheng Qin, Qian Qiao 等 13 位作者

原文摘要

Skills provide agents with reusable experience to guide future decisions without modifying the underlying model. However, coding trajectories are often entangled with task- and repository-specific contexts, making it challenging to distill procedural skills that generalize across diverse coding environments. To address this challenge, we present Hierarchical Skill Evolution (HSE), which progressively distills procedural knowledge from coding trajectories through hierarchical evidence aggregation. HSE first extracts task-level evidence from individual trajectories, then aggregates recurring evidence within the same repository to form repository-specific skill candidates, and finally consolidates consistent patterns across repositories into transferable skills across repositories. The evolution is driven by an external writer-evaluator loop that generates and assesses skills while keeping the underlying coding model frozen. The loop externally updates the writer and evaluator policies to improve subsequent skill generation and assessment. This hierarchy progressively filters task-specific details while preserving procedures that generalize across diverse coding environments. Successful trajectories yield reusable guidance for future tasks, while failed trajectories reveal recurring failure modes that guide avoidance and recovery. We evaluate HSE on SWE-bench Verified and DeepSWE, using GLM-5.2 across Pi and Claude Code harnesses and GPT-5.5 and Opus 4.8 with Pi. Across both benchmarks, 10 skills evolved solely from SWE-Gym improve coding performance across all evaluated settings, demonstrating transfer across benchmarks, coding models, and agent harnesses. We further evolve the skill library from target-benchmark trajectories without verifier feedback, relying solely on the learned evaluator for candidate assessment and achieving additional gains through benchmark-tailored skills.

cs.CL