@inproceedings{hong-etal-2026-framework,
title = "Framework and Benchmark for Code-Driven Agentic Testing in Web Development",
author = "Hong, Bin and
Zhang, Zhenchao and
He, Jiyuan and
Zhang, Kai and
Huang, Zhenya",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2075/",
pages = "47250--47279",
ISBN = "979-8-89176-459-0",
abstract = "End-to-end GUI testing is essential for verifying web applications, yet existing evaluations rely on predefined checklists and are confined to the data and frameworks of web generation benchmarks, leaving the bug-discovery ability of vision-language models (VLMs) systematically untested. We introduce \textbf{C}ode-driven \textbf{A}gentic \textbf{T}esting (CAT), a paradigm in which the agent writes Playwright code to drive the browser, gathers feedback, and autonomously explores web applications to uncover bugs. We instantiate CAT with CATJudge, an agentic framework that unifies Browser-Use and Computer-Use tools within a single environment and CATTest, a benchmark of 102 AI-generated web applications with carefully annotated bugs, built through close human-AI collaboration to feature complex interactions and subtle defects. Experiments with mainstream VLMs show that all evaluated models perform poorly, revealing a clear gap between current VLM capabilities and the demands of real-world testing in AI web development."
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%0 Conference Proceedings
%T Framework and Benchmark for Code-Driven Agentic Testing in Web Development
%A Hong, Bin
%A Zhang, Zhenchao
%A He, Jiyuan
%A Zhang, Kai
%A Huang, Zhenya
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F hong-etal-2026-framework
%X End-to-end GUI testing is essential for verifying web applications, yet existing evaluations rely on predefined checklists and are confined to the data and frameworks of web generation benchmarks, leaving the bug-discovery ability of vision-language models (VLMs) systematically untested. We introduce Code-driven Agentic Testing (CAT), a paradigm in which the agent writes Playwright code to drive the browser, gathers feedback, and autonomously explores web applications to uncover bugs. We instantiate CAT with CATJudge, an agentic framework that unifies Browser-Use and Computer-Use tools within a single environment and CATTest, a benchmark of 102 AI-generated web applications with carefully annotated bugs, built through close human-AI collaboration to feature complex interactions and subtle defects. Experiments with mainstream VLMs show that all evaluated models perform poorly, revealing a clear gap between current VLM capabilities and the demands of real-world testing in AI web development.
%U https://aclanthology.org/2026.emnlp-main.2075/
%P 47250-47279
Markdown (Informal)
[Framework and Benchmark for Code-Driven Agentic Testing in Web Development](https://aclanthology.org/2026.emnlp-main.2075/) (Hong et al., EMNLP 2026)
ACL