构建首个端到端网页自动化测试基准，评估大模型执行网页任务的可靠性。
WebTestBench: Evaluating Computer-Use Agents towards End-to-End Automated Web Testing
- 拆解测试为清单生成与缺陷检测两阶段，实现全流程自动化评估。
- 多款主流大模型在测试覆盖率、缺陷发现率和长流程交互上均表现不佳。
- 适合研究自动化测试、AI编程及软件质量验证的开发者与研究人员。
大型语言模型（LLMs）推动了编程范式变革，催生了“直觉编程”——用户仅通过自然语言指令即可构建完整项目甚至控制计算机。这一趋势促进了网页自动化开发，但也带来了如何自动验证网页功能是否可靠实现的新挑战。现有方法难以适应开放环境，依赖静态视觉相似性或预设检查清单，且忽略软件质量中的潜在逻辑约束。为此，我们提出 WebTestBench，一个用于评估端到端自动化网页测试的基准数据集。该基准覆盖多种网页应用类别，将测试过程分解为检查清单生成与缺陷检测两个级联子任务，并提出 WebTester 基线框架。对多个流行大模型的评估显示，其普遍存在测试不完整、缺陷检测瓶颈以及长周期交互不可靠等问题。这些结果揭示了当前计算机使用智能体能力与工业级部署需求之间的显著差距。我们希望 WebTestBench 能为推进端到端自动化网页测试提供关键洞察与指导。数据集与代码已开源：https://github.com/friedrichor/WebTestBench。
原文摘要 · Abstract (English)
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.
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