一句话：提出ReproPilot，用全局规划整合LLM长程语义指导与启发式信号，在保持复现效果的同时大幅提升一致性与降低token开销。
移动缺陷复现对调试至关重要，现有基于LLM的方法虽优于传统技术，但存在两个局限：过度依赖单次LLM建议动作而缺乏原则性规划，导致对不可靠或不一致的LLM输出鲁棒性差；频繁查询LLM且每次输出利用率低，造成大量token开销。为此，作者提出轻量框架ReproPilot，将全局规划与LLM语义推理相结合。LLM不再仅提供逐步动作建议，而是给出多样化、可复用的长程指导；全局规划机制将该指导与启发式信号融合，推理复现进度并全局决定探索哪些复现状态以及探索哪些轨迹，而非仅在当前UI页面上执行动作。在74个真实Android崩溃报告上的评估表明，该方法在所比较方法中取得最佳可靠性与成本权衡：一致性最高达80.14%，有效性达86.49%，复现时间减少2.32%–14.07%，相比最先进的LLM基线降低LLM token消耗65.39%–83.96%。
Mobile bug reproduction is essential to effective debugging in software engineering. Recent methods leveraging Large Language Models (LLMs) have shown significant performance gains over traditional techniques in automating this task. However, existing LLM-based methods remain constrained by two major limitations. First, they lack robustness to unreliable or inconsistent LLM outputs due to overreliance on a single LLM-suggested action without principled planning. Second, substantial token overhead arises from frequent LLM queries and the limited utilization of each query’s output capacity. To address these limitations, we propose ReproPilot, a lightweight framework that combines global planning with LLM-based semantic reasoning to reproduce mobile bugs. Specifically, the LLM provides diverse, reusable long-horizon guidance beyond step-wise action suggestions. This guidance is integrated with heuristic signals by a global planning mechanism to reason about reproduction progress and globally determine where to explore (i.e., which reproduction states) and what trajectories to explore, rather than merely executing actions on the current UI page at each iteration. As a result, ReproPilot improves the consistency and cost-efficiency of bug reproduction while maintaining competitive effectiveness. Evaluation on 74 real-world Android crash reports shows that our approach delivers the best reliability–cost trade-off among the evaluated methods, attaining the highest consistency (80.14%) with competitive effectiveness (86.49%), reducing reproduction time by 2.32%–14.07%, and lowering LLM token consumption by 65.39%–83.96% compared with state-of-the-art LLM-based baselines.
@inproceedings{paperbot12379,
title = {Mobile Bug Reproduction via Global State Reprioritization and LLM-Guided Trajectory Exploration},
author = {Dingbang Wang and Sidong Feng and William G. J. Halfond and Tingting Yu},
booktitle = {ASE},
year = {2026},
doi = {10.1145/3832783.3837495}
}