42 citations · 87 across the 9 of their papers we have counts for
9 papers
Figma2Code: Automating Multimodal Design to Code in the Wild
Yi Gui, Jiawan Zhang, Yina Wang +9
Front-end development constitutes a substantial portion of software engineering, yet converting design mockups into production-ready User Interface (UI) code remains tedious and co…
Scrub It Out! Erasing Sensitive Memorization in Code Language Models via Machine Unlearning
Zhaoyang Chu, Yao Wan, Zhikun Zhang +7
While Code Language Models (CLMs) have demonstrated superior performance in software engineering tasks such as code generation and summarization, recent empirical studies reveal a…
How to Select Pre-Trained Code Models for Reuse? A Learning Perspective
Zhangqian Bi, Yao Wan, Zhaoyang Chu +5
Pre-training a language model and then fine-tuning it has shown to be an efficient and effective technique for a wide range of code intelligence tasks, such as code generation, cod…
Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study
Yang Wu, Yao Wan, Hongyu Zhang +5
The Natural Language to Visualization (NL2Vis) task aims to transform natural-language descriptions into visual representations for a grounded table, enabling users to gain insight…
Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation
Zhaoyang Chu, Yao Wan, Qian Li +5
Vulnerability detection is crucial for ensuring the security and reliability of software systems. Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding…
Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach
Yao Wan, Guanghua Wan, Shijie Zhang +4
Recent years have witnessed significant progress in developing deep learning-based models for automated code completion. Although using source code in GitHub has been a common prac…
WebCode2M: A Real-World Dataset for Code Generation from Webpage Designs
Yi Gui, Zhen Li, Yao Wan +10
Automatically generating webpage code from webpage designs can significantly reduce the workload of front-end developers, and recent Multimodal Large Language Models (MLLMs) have s…
Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback
Zhangqian Bi, Yao Wan, Zheng Wang +7
Large Language Models (LLMs) have shown remarkable progress in automated code generation. Yet, LLM-generated code may contain errors in API usage, class, data structure, or missing…
Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit
Yao Wan, Yang He, Zhangqian Bi +6
Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and pr…