50 citations · 75 across the 19 of their papers we have counts for
12 papers · 1 filter
VisualRepair: Dynamic Tool Calling and Region Focusing for Visual Software Issue Repair
Jingyu Xiao, Zhongyi Zhang, Haoran Hou +4
Automated Program Repair (APR) has witnessed significant progress with the advent of Large Language Models (LLMs). However, as modern software systems increasingly expose rich grap…
From Runnable to Shippable: Multi-Agent Test-Driven Development for Generating Full-Stack Web Applications from Requirements
Yuxuan Wan, Tingshuo Liang, Jiakai Xu +3
Coding agents can generate runnable web applications, but their outputs frequently fail to satisfy functional requirements. Although test-driven development (TDD) offers a principl…
UIBenchKit: A unified toolkit for design-to-code model evaluation
Chinh T. Le, Trevor Ong Yee Siang, Jingyu Xiao +2
Recent years have seen substantial progress in automated design-to-code generation, with many methods proposed for generating HTML and CSS from webpage screenshots. However, the ab…
TREAT: A Code LLMs Trustworthiness / Reliability Evaluation and Testing Framework
Shuzheng Gao, Eric John Li, Man Ho Lam +5
Large foundation models are fundamentally transforming the software engineering landscape, demonstrating exceptional capabilities across diverse tasks such as code generation, debu…
Automatically Generating Web Applications from Requirements Via Multi-Agent Test-Driven Development
Yuxuan Wan, Tingshuo Liang, Jiakai Xu +3
Developing full-stack web applications is complex and time-intensive, demanding proficiency across diverse technologies and frameworks. Although recent advances in multimodal large…
EfficientUICoder: A Bidirectional Token Compression Framework for Efficient MLLM-Based UI Code Generation
Jingyu Xiao, Zhongyi Zhang, Yuxuan Wan +3
Multimodal Large Language Models have demonstrated exceptional performance in UI2Code tasks, significantly enhancing website development efficiency. However, these tasks incur subs…
DesignBench: A Comprehensive Benchmark for MLLM-based Front-end Code Generation
Jingyu Xiao, Ming Wang, Man Ho Lam +4
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in automated front-end engineering, e.g., generating UI code from visual designs. However, existi…
MRWeb: An Exploration of Generating Multi-Page Resource-Aware Web Code from UI Designs
Yuxuan Wan, Yi Dong, Jingyu Xiao +3
Multi-page websites dominate modern web development. However, existing design-to-code methods rely on simplified assumptions, limiting to single-page, self-contained webpages witho…
Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototyping
Jingyu Xiao, Yuxuan Wan, Yintong Huo +6
Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance on the design-to-code task, i.e., generating UI code from UI mock-ups. However, existing benchmark…
Automatically Generating UI Code from Screenshot: A Divide-and-Conquer-Based Approach
Yuxuan Wan, Chaozheng Wang, Yi Dong +4
Websites are critical in today's digital world, with over 1.11 billion currently active and approximately 252,000 new sites launched daily. Converting website layout design into fu…
New Job, New Gender? Measuring the Social Bias in Image Generation Models
Wenxuan Wang, Haonan Bai, Jen-tse Huang +5
Image generation models can generate or edit images from a given text. Recent advancements in image generation technology, exemplified by DALL-E and Midjourney, have been groundbre…
LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models
Yuxuan Wan, Wenxuan Wang, Yiliu Yang +5
We introduce LogicAsker, a novel approach for evaluating and enhancing the logical reasoning capabilities of large language models (LLMs) such as ChatGPT and GPT-4. Despite LLMs' p…