用AI代理自动测试网页，发现传统工具漏掉的可用性问题。
AI Agents for Web Testing: A Case Study in the Wild
- 用AI代理模拟真实用户操作网站
- 在120个学术个人网站中发现29个可用性问题
- 适合关注用户体验和自动化测试的研究者
自动化网页测试对保障用户体验和创造商业价值至关重要。传统方法主要关注代码覆盖率和负载测试，但难以捕捉复杂用户行为，导致许多可用性问题未被发现。大语言模型和AI代理的出现为网页测试带来新可能，可实现类人交互并具备通用可用性认知。本文提出WebProber——一个基于AI代理的网页测试原型框架。给定网址后，WebProber可自主探索网站，模拟真实用户操作，识别缺陷与可用性问题，并生成可读报告。我们在120个学术个人网站上开展案例研究，发现29个可用性问题，其中多数被传统工具遗漏。结果表明，基于代理的测试是未来发展方向，也为下一代以用户为中心的测试框架提供思路。
原文摘要 · Abstract (English)
Automated web testing plays a critical role in ensuring high-quality user experiences and delivering business value. Traditional approaches primarily focus on code coverage and load testing, but often fall short of capturing complex user behaviors, leaving many usability issues undetected. The emergence of large language models (LLM) and AI agents opens new possibilities for web testing by enabling human-like interaction with websites and a general awareness of common usability problems. In this work, we present WebProber, a prototype AI agent-based web testing framework. Given a URL, WebProber autonomously explores the website, simulating real user interactions, identifying bugs and usability issues, and producing a human-readable report. We evaluate WebProber through a case study of 120 academic personal websites, where it uncovered 29 usability issues--many of which were missed by traditional tools. Our findings highlight agent-based testing as a promising direction while outlining directions for developing next-generation, user-centered testing frameworks.
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