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.
Mengzhuo Chen, Zhe Liu, Chunyang Chen et al.· Proceedings of the ACM on So...· 0 citations
Platform-orchestrated agentic workflows have become a popular paradigm for developing LLM-based applications. However, their reliability remains a major challenge due to the uncertainty of LLM outputs, complex inter-node dependencies, and heterogeneous tool interactions. Existing agentic workflow optimization and agent enhancement methods primarily rely on trajectory-level feedback. Without explicitly identifying the underlying failure root causes, their resulting repair plans are often insufficiently targeted. We propose FlowFixer, a diagnosis-driven automated repair framework for agentic workflows. FlowFixer first transforms workflow executions into unified symbolic traces and performs symbolic inference to derive executable behavioral specifications that capture node correctness, temporal dependencies, and causal relationships. Based on specification verification, it conducts failure attribution and root cause analysis, and then generates targeted repair patches. To reduce verification costs, FlowFixer further employs a multi-dimensional pre-execution assessment to filter infeasible repairs before dynamic verification. We evaluate FlowFixer on workflow failures collected from three popular development platforms: Dify, Coze and n8n. Results show that FlowFixer achieves a repair success rate of 71.3%, outperforming state-of-the-art baselines by 11.9% to 27.6%. It also improves failure attribution accuracy by 4.8% to 33.1% and root cause analysis accuracy by 15.3% to 38.8%. This work offers a new perspective on reliable diagnosis and repair of agentic workflows through symbolic modeling and inference.
Xuyan Ma, Yawen Wang, Junjie Wang et al.· 0 citations