WebTestBench: Evaluating Computer-Use Agents towards End-to-End Automated Web Testing
This addresses the need for reliable automated web testing in software development, particularly for end-to-end verification, but it is incremental as it builds on existing testing paradigms with a new benchmark.
The authors tackled the problem of automatically verifying web functionalities in open-ended environments by introducing WebTestBench, a benchmark for evaluating end-to-end automated web testing, and found that current LLMs face severe challenges like insufficient test completeness and detection bottlenecks, exposing a gap from industrial deployment demands.
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.