The emergence of Large Language Models (LLMs) has catalyzed a paradigm shift in programming, giving rise to "vibe coding", where users can build complete projects and even control computers using natural language instructions. This paradigm has driven automated webpage development, but it introduces a new requirement about how to automatically verify whether the web functionalities are reliably implemented. Existing works struggle to adapt, relying on static visual similarity or predefined checklists that constrain their utility in open-ended environments. Furthermore, they overlook a vital aspect of software quality, namely latent logical constraints. To address these gaps, we introduce WebTestBench, a benchmark for evaluating end-to-end automated web testing. WebTestBench encompasses comprehensive dimensions across diverse web application categories. We decompose the testing process into two cascaded sub-tasks, checklist generation and defect detection, and propose WebTester, a baseline framework for this task. Evaluating popular LLMs with WebTester reveals severe challenges, including insufficient test completeness, detection bottlenecks, and long-horizon interaction unreliability. These findings expose a substantial gap between current computer-use agent capabilities and industrial-grade deployment demands. We hope that WebTestBench provides valuable insights and guidance for advancing end-to-end automated web testing. Our dataset and code are available at https://github.com/friedrichor/WebTestBench.

翻译：大型语言模型（LLM）的涌现引发了编程范式的革命，催生了"氛围编码"——用户可通过自然语言指令构建完整项目，甚至操控计算机。该范式推动了自动化网页开发进程，但随之而来的是新需求：如何自动验证网页功能的可靠实现？现有方法依赖静态视觉相似性或预定义检查清单，难以适应开放环境，且忽视了软件质量的核心维度——潜在逻辑约束。为弥合这些不足，我们提出WebTestBench——面向端到端自动化Web测试的评估基准。该基准涵盖多类Web应用的综合性维度，将测试流程解耦为级联子任务：清单生成与缺陷检测，并构建基线框架WebTester。通过WebTester评估主流LLM时，暴露出测试覆盖率不足、检测瓶颈及长程交互不可靠等严峻挑战，揭示了当前计算机使用智能体能力与工业级部署需求间的显著鸿沟。我们期望WebTestBench能为推进端到端自动化Web测试提供宝贵洞见与指导。数据集与代码已开源至https://github.com/friedrichor/WebTestBench。