MRWeb: An Exploration of Generating Multi-Page Resource-Aware Web Code from UI Designs

Fuente: arXiv
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Autori principali: Wan, Yuxuan, Dong, Yi, Xiao, Jingyu, Huo, Yintong, Wang, Wenxuan, Lyu, Michael R.
Natura: Preprint
Pubblicazione: 2024
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author Wan, Yuxuan
Dong, Yi
Xiao, Jingyu
Huo, Yintong
Wang, Wenxuan
Lyu, Michael R.
author_facet Wan, Yuxuan
Dong, Yi
Xiao, Jingyu
Huo, Yintong
Wang, Wenxuan
Lyu, Michael R.
contents Multi-page websites dominate modern web development. However, existing design-to-code methods rely on simplified assumptions, limiting to single-page, self-contained webpages without external resource connection. To address this gap, we introduce the Multi-Page Resource-Aware Webpage (MRWeb) generation task, which transforms UI designs into multi-page, functional web UIs with internal/external navigation, image loading, and backend routing. We propose a novel resource list data structure to track resources, links, and design components. Our study applies existing methods to the MRWeb problem using a newly curated dataset of 500 websites (300 synthetic, 200 real-world). Specifically, we identify the best metric to evaluate the similarity of the web UI, assess the impact of the resource list on MRWeb generation, analyze MLLM limitations, and evaluate the effectiveness of the MRWeb tool in real-world workflows. The results show that resource lists boost navigation functionality from 0% to 66%-80% while facilitating visual similarity. Our proposed metrics and evaluation framework provide new insights into MLLM performance on MRWeb tasks. We release the MRWeb tool, dataset, and evaluation framework to promote further research.
format Preprint
id arxiv_https___arxiv_org_abs_2412_15310
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle MRWeb: An Exploration of Generating Multi-Page Resource-Aware Web Code from UI Designs
Wan, Yuxuan
Dong, Yi
Xiao, Jingyu
Huo, Yintong
Wang, Wenxuan
Lyu, Michael R.
Software Engineering
Artificial Intelligence
Information Retrieval
Multi-page websites dominate modern web development. However, existing design-to-code methods rely on simplified assumptions, limiting to single-page, self-contained webpages without external resource connection. To address this gap, we introduce the Multi-Page Resource-Aware Webpage (MRWeb) generation task, which transforms UI designs into multi-page, functional web UIs with internal/external navigation, image loading, and backend routing. We propose a novel resource list data structure to track resources, links, and design components. Our study applies existing methods to the MRWeb problem using a newly curated dataset of 500 websites (300 synthetic, 200 real-world). Specifically, we identify the best metric to evaluate the similarity of the web UI, assess the impact of the resource list on MRWeb generation, analyze MLLM limitations, and evaluate the effectiveness of the MRWeb tool in real-world workflows. The results show that resource lists boost navigation functionality from 0% to 66%-80% while facilitating visual similarity. Our proposed metrics and evaluation framework provide new insights into MLLM performance on MRWeb tasks. We release the MRWeb tool, dataset, and evaluation framework to promote further research.
title MRWeb: An Exploration of Generating Multi-Page Resource-Aware Web Code from UI Designs
topic Software Engineering
Artificial Intelligence
Information Retrieval
url https://arxiv.org/abs/2412.15310