From Suspicious Signals to Crashes: Guiding Bug-Driven GUI Testing via Code-Inspired Tracing

Abstract

Mobile applications frequently suffer from crash bugs that are triggered under specific GUI interaction sequences. Existing automated GUI testing techniques mainly emphasize increasing coverage through diverse exploration strategies, but they often fail to reach the precise interaction contexts that lead to crashes, resulting in low bug detection efficiency. This paper proposes TraceDroid, a novel automated GUI testing approach that leverages suspicious code-level signals to guide dynamic exploration. Instead of treating static analysis as an independent detection method, TraceDroid uses heuristic rules distilled from real crash reports to detect suspicious code segments, associate them with GUI widgets, and collect code-level interaction signals. It then constructs the Activity Transition Graph (ATG), performs rough path generation, and employs LLM-based executable path completion to produce a set of suspicious paths. Finally, TraceDroid executes these paths through global path planning, local path generation, and execution-aware monitoring to efficiently expose crashes. We evaluate TraceDroid on 70 real crash bugs across 42 open-source apps, comparing it with 15 state-of-the-art baselines. TraceDroid achieves the best performance, with a recall of 77%, exceeding the best baseline by 28%, while maintaining comparable or higher coverage. Furthermore, TraceDroid successfully detects 21 previously unknown crash bugs in 116 popular Google Play apps, of which 15 have been fixed and 6 confirmed by developers, demonstrating its effectiveness in real-world scenarios.