AI 中文总结
研究针对移动平台语音助手与应用行为契合度低的问题，提出由语言模型驱动、利用图形用户界面测试代码自动开发特定应用语音助手的方法，经安卓原型验证，可复用测试代码实现语音助手合成并指明相关研究方向。
AI 中文摘要
语音助手广泛应用于移动平台，但大多作为系统级服务，与特定应用行为契合度低。在应用层面实现语音交互需开发者手动重写应用逻辑，成本高昂。我们提出一种由语言模型驱动的方法，通过重新利用图形用户界面测试代码来自动开发特定应用语音助手，该代码编码了应用功能的行为保留和可执行规范。本文提出一种视角，即大语言模型将图形用户界面测试重新解释为应用行为与对话交互之间的桥梁。通过将测试方法转换为特定应用语音助手工件，我们的方法使语音助手直接基于现有应用逻辑而非外部规范。我们通过安卓上的研究原型AppVA展示了这一愿景。我们对五个开源应用的初步结果表明，图形用户界面测试代码可在测试之外复用，实现特定应用语音助手的合成，并突出了软件测试、交互设计和基于大语言模型的自动化交叉领域的更广泛研究方向。
英文摘要
Voice assistants are widely deployed on mobile platforms, yet most are designed as system-level services that remain poorly aligned with application-specific behavior. As a result, enabling voice interaction at the app level requires developers to manually reimplement application logic, leading to high development and maintenance costs. We propose an LLM-driven approach to automating the development of app-specific voice assistants by repurposing GUI test code, which encodes behavior-preserving, executable specifications of application functionality. In this paper, we present a perspective in which large language models reinterpret GUI tests as bridges between application behavior and conversational interaction. By transforming test methods into app-specific VA artifacts, such as voice intents, capability descriptions, and executable action plans, our approach grounds voice assistants directly in existing application logic rather than external specifications. We illustrate this vision through AppVA, a research prototype on Android. Our preliminary results across five open-source applications suggest that GUI test code can be reused beyond testing, enabling the synthesis of app-specific voice assistants and highlighting a broader research direction at the intersection of software testing, interaction design, and LLM-enabled automation.
