WebTestBench: Evaluating Computer-Use Agents towards End-to-End Automated Web Testing

TL;DR

WebTestBench is a comprehensive benchmark designed to evaluate and improve end-to-end automated web testing by assessing large language models' ability to generate checklists and detect defects in diverse web applications.

Contribution

The paper introduces WebTestBench, a new benchmark for evaluating automated web testing, and proposes WebTester, a baseline framework to address current challenges in the field.

Findings

LLMs show limited test completeness and detection capabilities.

Current automated testing methods face reliability issues in open-ended environments.

Significant gap exists between current capabilities and industrial requirements.

Abstract

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.