56 citations · 249 across the 39 of their papers we have counts for
39 papers
Understanding and Improving Model Editing for Secure Code Generation
Weifeng Sun, Quanjun Zhang, Yuchen Chen +3
Large language models (LLMs) are widely used for code generation, yet they can reproduce vulnerable implementations learned from insecure training patterns. Prior work has mainly e…
EvoRepair: Enhancing Vulnerability Repair Agents Through Experience-Based Self-Evolution
Haichuan Hu, Guoqing Xie, Quanjun Zhang +5
Large Language Models (LLMs) have shown promise for automated vulnerability repair (AVR), but they still face several limitations, including the lack of intra-vulnerability experie…
ATTest: Agent-Driven Tensor Testing for Deep Learning Library Modules
Zhengyu Zhan, Ye Shang, Jiawei Liu +3
The unit testing of Deep Learning (DL) libraries is challenging due to complex numerical semantics and implicit tensor constraints. Traditional Search-Based Software Testing (SBST)…
SGAgent: Suggestion-Guided LLM-Based Multi-Agent Framework for Repository-Level Software Repair
Quanjun Zhang, Chengyu Gao, Yu Han +4
Large Language Models (LLMs) have enabled intelligent agents that autonomously interact with environments and invoke external tools. Recently, agent-based software repair has drawn…
Large Language Models for Unit Testing: A Systematic Literature Review
Quanjun Zhang, Chunrong Fang, Siqi Gu +3
Unit testing is a fundamental practice in modern software engineering, with the aim of ensuring the correctness, maintainability, and reliability of individual software components.…
Improving Retrieval-Augmented Deep Assertion Generation via Joint Training
Quanjun Zhang, Chunrong Fang, Yi Zheng +7
Unit testing attempts to validate the correctness of basic units of the software system under test and has a crucial role in software development and testing. Very recent work prop…