WebCraftBench: Evaluating Web Application Generation from a Software Testing Perspective
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Abstract (arXiv)
Human evaluation provides a direct measure of the quality of LLM-generated web applications. However, fitting human judgments through automated evaluation remains challenging. Static benchmarks can credit functionality that exists in source code but is unreachable at runtime. Interactive benchmarks exercise the application, yet incomplete exploration can cause them to miss implemented functionality and confound application defects with agent execution failures. To address these limitations, we propose WebCraftBench, an interactive benchmark for evaluating web application generation from a software testing perspective. WebCraftBench instruments each generated application and uses code coverage to guide an agent in exploring its functionality through user-simulated interactions. It then abstracts the interaction trace into a state-transition graph and evaluates the application along three dimensions: visual aesthetics, usability, and requirement alignment. By separating exploration from scoring, WebCraftBench collects runtime evidence without constraining exploration to predefined acceptance criteria. WebCraftBench comprises 369 real-world user requirements and 5,088 acceptance criteria. Evaluation of 17 frontier LLMs reveals distinct strengths across the three dimensions, with no model leading on every dimension. On 197 validated sessions sampled from an internal arena, WebCraftBench achieves 85.3% agreement with human preferences, with agreement generally increasing as the score difference between paired applications grows. Further experiments show that coverage guidance improves exploration coverage and the model rankings remain stable when the judge model is replaced.
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References · 24
- 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,463
- 2017pix2code: Generating Code from a Graphical User Interface Screenshotcited by 114
- 2025WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratchcited by 72
- 2025ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluationcited by 42
- 2023Make LLM a Testing Expert: Bringing Human-like Interaction to Mobile GUI Testing via Functionality-aware Decisionscited by 41
- 2025Web-Bench: A LLM Code Benchmark Based on Web Standards and Frameworkscited by 40
- 2025FullFront: Benchmarking MLLMs Across the Full Front-End Engineering Workflowcited by 28
- 2025FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluationcited by 20
- 2021Automatic Web Testing using Curiosity-Driven Reinforcement Learningcited by 19
- 2026WebCoderBench: Benchmarking Web Application Generation with Comprehensive and Interpretable Evaluation Metricscited by 18
- 2025AWorld: Orchestrating the Training Recipe for Agentic AIcited by 17
- 2025FronTalk: Benchmarking Front-End Development as Conversational Code Generation with Multi-Modal Feedbackcited by 14
12 more
- 2026WebGameBench: Requirement-to-Application Evaluation for Coding Agents via Browser-Native Gamescited by 12
- 2025WebSight: A Vision-First Architecture for Robust Web Agentscited by 8
- 2026MiniAppBench: Evaluating the Shift from Text to Interactive HTML Responses in LLM-Powered Assistantscited by 8
- 2025ResTest: Targeted Coverage of Residual Not-Covered Code Using Large Language Models for Web GUI Testingcited by 7
- 2026WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Modelscited by 6
- 2026Cookie-Bench: Continuous On-screen Key Interaction Evaluation for Web Generationcited by 3
- 2026I-WebGenBench : Evaluating Interactivity in LLM-Generated Scientific Web Applicationscited by 3
- 2021Fragment-Based Test Generation For Web Appscited by 2
- 2025Multimodal graph representation learning for website generation based on visual sketchcited by 2
- 2025Deep Reinforcement Learning for Automated Web GUI Testingcited by 2
- 2026WebRISE: Requirement-Induced State Evaluation for MLLM-Generated Web Artifactscited by 2
- 2026Understanding Automated Web GUI Testing: An Empirical Study Across Exploration Strategies and State Abstractionscited by 1
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