Mobile Bug Reproduction via Global State Reprioritization and LLM-Guided Trajectory Exploration

Abstract

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.