# ComUICoder: Component-based Reusable UI Code Generation for Complex Websites via Semantic Segmentation and Element-wise Feedback

Source: [ComUICoder: Component-based Reusable UI Code Generation for Complex Websites via Semantic Segmentation and Element-wise Feedback](<https://www.sota2.com/research/paper/699d30be6017c4460ffab707>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-25T05:00:07.533Z

Authors: Jingyu Xiao, Jiantong Qin, Shuoqi Li, Man Ho Lam, Yuxuan Wan, Jen-tse Huang, Yintong Huo, Michael R. Lyu

Published: 2026-02-22T17:17:16.000Z

## Abstract

Multimodal Large Language Models (MLLMs) have demonstrated strong performance on the UI-to-code task, which aims to generate UI code from design mock-ups. However, when applied to long and complex websites, they often struggle with fragmented segmentation, redundant code generation for repetitive components, and frequent UI inconsistencies. To systematically investigate and address these challenges, we introduce ComUIBench, a new multi-page complex webpage benchmark with component annotations, designed to evaluate MLLMs' ability to generate reusable UI code in realistic website scenarios. Building upon this benchmark, we propose ComUICoder, a component-based UI code generation framework that emphasizes semantic-aware segmentation, code reuse, and fine-grained refinement. Specifically, ComUICoder incorporates (1) Hybrid Semantic-aware Block Segmentation for accurate UI semantic coherent block detection, (2) Visual-aware Graph-based Block Merge to consolidate structurally similar components within and across webpages for reusable implementation, and (3) Priority-based Element-wise Feedback to refine generated code and reduce element-level inconsistencies. Extensive experiments demonstrate that ComUICoder significantly improves overall generation quality and code reusability on complex multipage websites. Our datasets and code are publicly available at https://github.com/WebPAI/ComUICoder.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2602.19276>)

[PDF](<https://arxiv.org/pdf/2602.19276.pdf>)