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Prototype2Code: End-to-end Front-end Code Generation from UI Design Prototypes

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arxiv 2405.04975 v1 pith:QMUYUHVJ submitted 2024-05-08 cs.SE

classification cs.SE
keywords codeprototype2codedesignfront-endprototypesgeneratedgenerationbusiness
verification ladder T0 review T1 audit T2 compute T3 formal

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UI-to-code technology has streamlined the front-end development process, reducing repetitive tasks for engineers. prior research mainly use design prototypes as inputs, with the effectiveness of the generated code heavily dependent on these prototypes' quality, leading to compromised robustness. Moreover, these approaches also exhibit shortcomings in code quality, including issues such as disorganized UI structures and the inability to support responsive layouts. To address these challenges, we introduce Prototype2Code, which achieves end-to-end front-end code generation with business demands. For Prototype2Code, we incorporate design linting into the workflow, addressing the detection of fragmented elements and perceptual groups, enhancing the robustness of the generated outcomes. By optimizing the hierarchical structure and intelligently recognizing UI element types, Prototype2Code generates code that is more readable and structurally clearer. To meet responsive design requirements, Prototype2Code primarily supports flexbox layout model, ensuring code compatibility across various device sizes. To validate the efficacy, we compare Prototype2Code with the commercial code generation platform CodeFun and Screenshot-to-code based on GPT-4 with vision. Employing structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean squared error (MSE) for visual similarity assessment, Prototype2Code's rendered UI effects align most closely with the design prototypes, exhibiting the minimal errors. We also conduct a user study with five experienced front-end engineers, inviting them to review and revise code generated by the three methods. As a result, Prototype2Code surpasses other methods in readability, usability, and maintainability, better meeting the business needs of industrial development.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Pattern over Pixels: Measuring Pattern Completion Bias in Multimodal Code Generation

    cs.SE 2026-08 conditional novelty 6.0 of 10

    On a new 1,440-screenshot fill-in-the-blank benchmark, five frontier multimodal models default to repeated UI patterns instead of visual deviations, with mean bias rates from 69.8% on card widths to 80.2% on font sizes.

  2. MLLM-Based UI2Code Automation Guided by UI Layout Information

    cs.SE 2025-06 conditional novelty 6.0 of 10

    LayoutCoder improves screenshot-to-code generation on real websites by parsing the layout into a tree, generating code per region, and fusing it deterministically, beating the best baseline by 10.14 BLEU and 3.95 CLIP...

  3. Improving Existing Optimization Algorithms with LLMs

    cs.AI 2025-02 conditional novelty 5.0 of 10

    A GPT-4o-proposed age-aware heuristic for the CMSA metaheuristic outperforms the expert degree-based heuristic on Maximum Independent Set instances across three graph families.

  4. Fragmented Layer Grouping in GUI Designs Through Graph Learning Based on Multimodal Information

    cs.SE 2024-12 conditional novelty 4.0 of 10

    A self-attention graph neural network that classifies GUI layers and predicts component bounding boxes groups fragmented layers more accurately than prior methods.

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