Mobile Graphical User Interface (GUI) agents aim to autonomously complete tasks within or across apps based on user instructions. While recent Multimodal Large Language Models (MLLMs) enable these agents to interpret UI screens and perform actions, existing agents remain fundamentally reactive. They reason over the current UI screen but lack a structured representation of the app navigation flow, lim- iting GUI agents’ ability to understand execution context, detect unexpected execution results, and recover from errors. We introduce Agent-SAMA, a state-aware multi-agent framework that models app execution as a Finite State Machine (FSM), treating UI screens as states and user actions as transitions. Agent-SAMA implements four specialized agents that collaboratively construct and use FSMs in real time to guide task planning, execution verification, and recovery. We evaluate Agent-SAMA on two types of benchmarks: cross- app (Mobile-Eval-E, SPA-Bench) and mostly single-app (AndroidWorld). On Mobile-Eval-E, Agent-SAMA achieves an 84.0% success rate and a 71.9% recovery rate. On SPA-Bench, it reaches an 80.0% success rate with a 66.7% recovery rate. Compared to prior methods, Agent-SAMA improves task success by up to 12% and recovery success by 13.8%. On AndroidWorld, Agent-SAMA achieves a 63.7% success rate, outperforming the baselines. Our results demonstrate that structured state modeling enhances robustness and can serve as a lightweight, model-agnostic memory layer for future GUI agents.

Large Language Models (LLMs) have become a focal point of research across various domains, including software engineering, where their capabilities are increasingly leveraged. Recent studies have explored the integration of LLMs into software development tools and frameworks, revealing their potential to enhance performance in text and code-related tasks. Log level is a key part of a logging statement that allows software developers control the information recorded during system runtime. Given that log messages often mix natural language with code-like variables, LLMs' language translation abilities could be applied to determine the suitable verbosity level for logging statements. In this paper, we undertake a detailed empirical analysis to investigate the impact of characteristics and learning paradigms on the performance of 12 open-source LLMs in log level suggestion. We opted for open-source models because they enable us to utilize in-house code while effectively protecting sensitive information and maintaining data security. We examine several prompting strategies, including Zero-shot, Few-shot, and fine-tuning techniques, across different LLMs to identify the most effective combinations for accurate log level suggestions. Our research is supported by experiments conducted on 9 large-scale Java systems. The results indicate that although smaller LLMs can perform effectively with appropriate instruction and suitable techniques, there is still considerable potential for improvement in their ability to suggest log levels.

Mobile operating systems (OS) are frequently updated, but such updates can unintentionally degrade user experience by introducing performance regressions. Existing detection techniques often rely on system-level metrics (e.g., CPU or memory usage) or focus on specific OS components, which may miss regressions actually perceived by users—such as slower responses or UI stutters. To address this gap, we present MobileUPReg, a black-box framework for detecting user-perceived performance regressions across OS versions. MobileUPReg runs the same apps under different OS versions and compares user-perceived performance metrics—response time, finish time, launch time, and dropped frames—to identify regressions that are truly perceptible to users. In a large-scale study, MobileUPReg achieves high accuracy in extracting user-perceived metrics and detects user-perceived regressions with 0.96 precision, 0.91 recall, and 0.93 F1-score—significantly outperforming a statistical baseline using the Wilcoxon rank-sum test and Cliff’s Delta. MobileUPReg has been deployed in an industrial CI pipeline, where it analyzes thousands of screencasts across hundreds of apps daily and has uncovered regressions missed by traditional tools. These results demonstrate that MobileUPReg enables accurate, scalable, and perceptually aligned regression detection for mobile OS validation.