Large Language Models (LLMs) have demonstrated remarkable proficiency in code generation, yet their application to Property-Based Testing (PBT) remains fraught with a superficiality gap. While LLMs can readily generate syntactically correct tests, they often struggle to bridge the semantic gap between code implementation and its intended invariant logic, resulting in weak properties that provide a false sense of security. To address this, we introduce PROBE, an agentic framework that hardens software properties through Adversarial Refinement. Unlike traditional generation approaches, PROBE treats test generation as a game of semantic asymmetry: it employs a Validator agent to actively generate counter-implementations, which are semantically incorrect codes that satisfy the generated property, to expose loopholes in the specification. Furthermore, PROBE constructs a cross-functional semantic graph to capture deep dependencies often missed by local analysis. Extensive evaluation reveals that PROBE increases mutation scores by 9.79% over baselines. In real-world deployment, PROBE identified 45 previously unknown bugs in top-tier libraries that have been confirmed by developers, demonstrating its ability to uncover deep semantic defects.

Mobile task automation is an emerging technology that leverages AI to automatically execute routine tasks by users’ commands on mobile devices like Android, thus enhancing efficiency and productivity. While large language models (LLMs) excel at general mobile tasks through training on massive datasets, they struggle with app-specific workflows. To solve this problem, we designed UI Map, a structured representation of target app’s UI information. We further propose a UI Map-guided LLM-based approach UICompass to automate mobile tasks. Specifically, UICompass first leverages static analysis and LLMs to automatically build UI Map from either source codes of apps or byte codes (i.e., APK packages). During task execution, UICompass mines the task-relevant information from UI Map to feed into the LLMs, generate a planned paths, and adaptively adjust the path based on the actual app state and action history. Experimental results demonstrate that UICompass achieves a 15.87% higher task executing success rate than SOTA approaches. Even when only APK is available, UICompass maintains superior performance, demonstrating its applicability to closed-source apps.