Cover Image

Figma2Code: Automating Multimodal Design to Code in the Wild

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gui, Yi, Zhang, Jiawan, Wang, Yina, Ma, Tianran, Wan, Yao, He, Shilin, Chen, Dongping, Zhao, Zhou, Jiang, Wenbin, Shi, Xuanhua, Jin, Hai, Yu, Philip S
Format:	Preprint
Published:	2026
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LaTCoder: Converting Webpage Design to Code with Layout-as-Thought
by: Gui, Yi, et al.
Published: (2025)

UICopilot: Automating UI Synthesis via Hierarchical Code Generation from Webpage Designs
by: Gui, Yi, et al.
Published: (2025)

WebCode2M: A Real-World Dataset for Code Generation from Webpage Designs
by: Gui, Yi, et al.
Published: (2024)

code_transformed: The Influence of Large Language Models on Code
by: Xu, Yuliang, et al.
Published: (2025)

Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback
by: Bi, Zhangqian, et al.
Published: (2024)

CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scale
by: Wang, Chenlong, et al.
Published: (2025)

Can Large Language Models Serve as Evaluators for Code Summarization?
by: Wu, Yang, et al.
Published: (2024)

Bridging Code Graphs and Large Language Models for Better Code Understanding
by: Chen, Zeqi, et al.
Published: (2025)

Scrub It Out! Erasing Sensitive Memorization in Code Language Models via Machine Unlearning
by: Chu, Zhaoyang, et al.
Published: (2025)

Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach
by: Wan, Yao, et al.
Published: (2024)

Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit
by: Wan, Yao, et al.
Published: (2023)

PSD2Code: Automated Front-End Code Generation from Design Files via Multimodal Large Language Models
by: Chen, Yongxi, et al.
Published: (2025)

Toward Automated and Trustworthy Scientific Analysis and Visualization with LLM-Generated Code
by: Chakroborti, Apu Kumar, et al.
Published: (2025)

MUCOCO: Automated Consistency Testing of Code LLMs
by: Chou, Chua Jin, et al.
Published: (2026)

WildCode: An Empirical Analysis of Code Generated by ChatGPT
by: Khanmohammadi, Kobra, et al.
Published: (2025)

CodeScore: Evaluating Code Generation by Learning Code Execution
by: Dong, Yihong, et al.
Published: (2023)

Process-Supervised Reinforcement Learning for Code Generation
by: Ye, Yufan, et al.
Published: (2025)

Bridging Design and Development with Automated Declarative UI Code Generation
by: Zhou, Ting, et al.
Published: (2024)

When is Generated Code Difficult to Comprehend? Assessing AI Agent Python Code Proficiency in the Wild
by: Temkulkiat, Nanthit, et al.
Published: (2026)

Improving Automated Code Reviews: Learning from Experience
by: Lin, Hong Yi, et al.
Published: (2024)

How to Select Pre-Trained Code Models for Reuse? A Learning Perspective
by: Bi, Zhangqian, et al.
Published: (2025)

EffiSkill: Agent Skill Based Automated Code Efficiency Optimization
by: Wang, Zimu, et al.
Published: (2026)

Decoding Human-LLM Collaboration in Coding: An Empirical Study of Multi-Turn Conversations in the Wild
by: Zhang, Binquan, et al.
Published: (2025)

CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges
by: Zhang, Kechi, et al.
Published: (2024)

Greening Large Language Models of Code
by: Shi, Jieke, et al.
Published: (2023)

Automated Code Review In Practice
by: Cihan, Umut, et al.
Published: (2024)

Inducing Vulnerable Code Generation in LLM Coding Assistants
by: Zeng, Binqi, et al.
Published: (2025)

Copilot Arena: A Platform for Code LLM Evaluation in the Wild
by: Chi, Wayne, et al.
Published: (2025)

COFFE: A Code Efficiency Benchmark for Code Generation
by: Peng, Yun, et al.
Published: (2025)

ACECode: A Reinforcement Learning Framework for Aligning Code Efficiency and Correctness in Code Language Models
by: Yang, Chengran, et al.
Published: (2024)

SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents
by: Wang, Yuhang, et al.
Published: (2026)

Automated Code Review Assignments: An Alternative Perspective of Code Ownership on GitHub
by: Lulla, Jai Lal, et al.
Published: (2025)

Sifting through the Chaff: On Utilizing Execution Feedback for Ranking the Generated Code Candidates
by: Sun, Zhihong, et al.
Published: (2024)

Automated Repair of Ambiguous Problem Descriptions for LLM-Based Code Generation
by: Jia, Haoxiang, et al.
Published: (2025)

CodeScore-R: An Automated Robustness Metric for Assessing the FunctionalCorrectness of Code Synthesis
by: Yang, Guang, et al.
Published: (2024)

Improving Automated Secure Code Reviews: A Synthetic Dataset for Code Vulnerability Flaws
by: Centellas-Claros, Leonardo, et al.
Published: (2025)

AI Code in the Wild: Measuring Security Risks and Ecosystem Shifts of AI-Generated Code in Modern Software
by: Wang, Bin, et al.
Published: (2025)

Flow2Code: Evaluating Large Language Models for Flowchart-based Code Generation Capability
by: He, Mengliang, et al.
Published: (2025)

Previously on... Automating Code Review
by: Heumüller, Robert, et al.
Published: (2025)

WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Models
by: Lei, Xinping, et al.
Published: (2026)