26 citations · 52 across the 22 of their papers we have counts for
17 papers · 1 filter
PracRepair: LLM-Empowered Automated Program Repair Inspired by Human-Like Debugging Practices
Yu Cheng, Zhongxin Liu, Zhenchang Xing +3
As software systems grow in scale and complexity, debugging and repair remain costly and time-consuming. Large language models (LLMs) have advanced automated program repair (APR),…
SolAgent: A Specialized Multi-Agent Framework for Solidity Code Generation
Wei Chen, Zhiyuan Peng, Xin Yin +4
Smart contracts are the backbone of the decentralized web, yet ensuring their functional correctness and security remains a critical challenge. While Large Language Models (LLMs) h…
Navigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Models
Dianshu Liao, Xin Yin, Shidong Pan +3
Unit testing is essential for software quality assurance, yet writing and maintaining tests remains time-consuming and error-prone. To address this challenge, researchers have prop…
RepoTransAgent: Multi-Agent LLM Framework for Repository-Aware Code Translation
Ziqi Guan, Xin Yin, Zhiyuan Peng +1
Repository-aware code translation is critical for modernizing legacy systems, enhancing maintainability, and enabling interoperability across diverse programming languages. While r…
Detecting LLM-generated Code with Subtle Modification by Adversarial Training
Xin Yin, Xinrui Li, Chao Ni +2
With the rapid development of Large Language Models (LLMs), their powerful code-generation capabilities have been widely applied in tasks like code completion and automated develop…
Learning to Align Human Code Preferences
Xin Yin, Chao Ni, Xiaohu Yang
Large Language Models (LLMs) have demonstrated remarkable potential in automating software development tasks. While recent advances leverage Supervised Fine-Tuning (SFT) and Direct…
A Preference-Driven Methodology for High-Quality Solidity Code Generation
Zhiyuan Peng, Xin Yin, Chenhao Ying +2
While Large Language Models (LLMs) have demonstrated remarkable progress in generating functionally correct Solidity code, they continue to face critical challenges in producing ga…
Enhancing LLM's Ability to Generate More Repository-Aware Unit Tests Through Precise Contextual Information Injection
Xin Yin, Chao Ni, Xinrui Li +3
Though many learning-based approaches have been proposed for unit test generation and achieved remarkable performance, they still have limitations in relying on task-specific datas…
What You See Is What You Get: Attention-based Self-guided Automatic Unit Test Generation
Xin Yin, Chao Ni, Xiaodan Xu +1
Software defects heavily affect software's functionalities and may cause huge losses. Recently, many AI-based approaches have been proposed to detect defects, which can be divided…
Distinguishing LLM-generated from Human-written Code by Contrastive Learning
Xiaodan Xu, Chao Ni, Xinrong Guo +4
Large language models (LLMs), such as ChatGPT released by OpenAI, have attracted significant attention from both industry and academia due to their demonstrated ability to generate…
Learning-based Models for Vulnerability Detection: An Extensive Study
Chao Ni, Liyu Shen, Xiaodan Xu +2
Though many deep learning-based models have made great progress in vulnerability detection, we have no good understanding of these models, which limits the further advancement of m…
Improving the Ability of Pre-trained Language Model by Imparting Large Language Model's Experience
Xin Yin, Chao Ni, Xiaodan Xu +2
Large Language Models (LLMs) and pre-trained Language Models (LMs) have achieved impressive success on many software engineering tasks (e.g., code completion and code generation).…
ThinkRepair: Self-Directed Automated Program Repair
Xin Yin, Chao Ni, Shaohua Wang +3
Though many approaches have been proposed for Automated Program Repair (APR) and indeed achieved remarkable performance, they still have limitations in fixing bugs that require ana…
Rectifier: Code Translation with Corrector via LLMs
Xin Yin, Chao Ni, Tien N. Nguyen +2
Software migration is garnering increasing attention with the evolution of software and society. Early studies mainly relied on handcrafted translation rules to translate between t…
CasModaTest: A Cascaded and Model-agnostic Self-directed Framework for Unit Test Generation
Chao Ni, Xiaoya Wang, Liushan Chen +4
Though many machine learning (ML)-based unit testing generation approaches have been proposed and indeed achieved remarkable performance, they still have several limitations in eff…
Natural Is The Best: Model-Agnostic Code Simplification for Pre-trained Large Language Models
Yan Wang, Xiaoning Li, Tien Nguyen +3
Pre-trained Large Language Models (LLM) have achieved remarkable successes in several domains. However, code-oriented LLMs are heavy in computational complexity, and quadratically…
Multitask-based Evaluation of Open-Source LLM on Software Vulnerability
Xin Yin, Chao Ni, Shaohua Wang
This paper proposes a pipeline for quantitatively evaluating interactive Large Language Models (LLMs) using publicly available datasets. We carry out an extensive technical evaluat…