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…
Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models
Quanjun Zhang, Chunrong Fang, Yi Zheng +7
Unit testing validates the correctness of the units of the software system under test and serves as the cornerstone in improving software quality and reliability. To reduce manual…
A Large-scale Empirical Study on Fine-tuning Large Language Models for Unit Testing
Ye Shang, Quanjun Zhang, Chunrong Fang +3
Unit testing plays a pivotal role in software development, improving software quality and reliability. However, generating effective test cases manually is time-consuming, promptin…
Security of Language Models for Code: A Systematic Literature Review
Yuchen Chen, Weisong Sun, Chunrong Fang +7
Language models for code (CodeLMs) have emerged as powerful tools for code-related tasks, outperforming traditional methods and standard machine learning approaches. However, these…
TestBench: Evaluating Class-Level Test Case Generation Capability of Large Language Models
Quanjun Zhang, Ye Shang, Chunrong Fang +3
Software testing is a crucial phase in the software life cycle, helping identify potential risks and reduce maintenance costs. With the advancement of Large Language Models (LLMs),…
No Man is an Island: Towards Fully Automatic Programming by Code Search, Code Generation and Program Repair
Quanjun Zhang, Chunrong Fang, Ye Shang +3
Automatic programming attempts to minimize human intervention in the generation of executable code, and has been a long-standing challenge in the software engineering community. To…
Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs
Haichuan Hu, Ye Shang, Guolin Xu +2
LLMs have long demonstrated remarkable effectiveness in automatic program repair (APR), with OpenAI's ChatGPT being one of the most widely used models in this domain. Through conti…
Eliminating Backdoors in Neural Code Models for Secure Code Understanding
Weisong Sun, Yuchen Chen, Chunrong Fang +7
Neural code models (NCMs) have been widely used to address various code understanding tasks, such as defect detection. However, numerous recent studies reveal that such models are…
TestART: Improving LLM-based Unit Testing via Co-evolution of Automated Generation and Repair Iteration
Siqi Gu, Quanjun Zhang, Kecheng Li +5
Unit testing is crucial for detecting bugs in individual program units but consumes time and effort. Recently, large language models (LLMs) have demonstrated remarkable capabilitie…
ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization
Chunrong Fang, Weisong Sun, Yuchen Chen +7
(Source) code summarization aims to automatically generate succinct natural language summaries for given code snippets. Such summaries play a significant role in promoting develope…
A Systematic Literature Review on Large Language Models for Automated Program Repair
Quanjun Zhang, Chunrong Fang, Yang Xie +4
Automated Program Repair (APR) attempts to patch software bugs and reduce manual debugging efforts. Very recently, with the advances in Large Language Models (LLMs), an increasing…
Pre-trained Model-based Actionable Warning Identification: A Feasibility Study
Xiuting Ge, Chunrong Fang, Quanjun Zhang +8
Actionable Warning Identification (AWI) plays a pivotal role in improving the usability of static code analyzers. Currently, Machine Learning (ML)-based AWI approaches, which mainl…
Machine Translation Testing via Syntactic Tree Pruning
Quanjun Zhang, Juan Zhai, Chunrong Fang +4
Machine translation systems have been widely adopted in our daily life, making life easier and more convenient. Unfortunately, erroneous translations may result in severe consequen…
A Prompt Learning Framework for Source Code Summarization
Tingting Xu, Yun Miao, Chunrong Fang +8
(Source) code summarization is the task of automatically generating natural language summaries (also called comments) for given code snippets. Recently, with the successful applica…
A Survey on Large Language Models for Software Engineering
Quanjun Zhang, Chunrong Fang, Yang Xie +5
Software Engineering (SE) is the systematic design, development, maintenance, and management of software applications underpinning the digital infrastructure of our modern world. V…
ACTesting: Automated Cross-modal Testing Method of Text-to-Image Software
Siqi Gu, Chunrong Fang, Quanjun Zhang +1
Recently, creative generative artificial intelligence software has emerged as a pivotal assistant, enabling users to generate content and seek inspiration rapidly. Text-to-Image (T…
Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?
Weisong Sun, Chunrong Fang, Yun Miao +8
Programming language understanding and representation (a.k.a code representation learning) has always been a hot and challenging task in software engineering. It aims to apply deep…
A Survey of Source Code Search: A 3-Dimensional Perspective
Weisong Sun, Chunrong Fang, Yifei Ge +6
(Source) code search is widely concerned by software engineering researchers because it can improve the productivity and quality of software development. Given a functionality requ…
Vision-Based Mobile App GUI Testing: A Survey
Shengcheng Yu, Chunrong Fang, Ziyuan Tuo +4
Graphical User Interface (GUI) has become one of the most significant parts of mobile applications (apps). It is a direct bridge between mobile apps and end users, which directly a…