22 citations · 39 across the 9 of their papers we have counts for
9 papers
A Comprehensive Study of Implementation Bugs in Multi-modal Agents
Suwan Li, Lei Bu, Shangqing Liu +5
Multi-Modal Agents (M-agents), empowered by Large Language Models (LLMs), excel in various complex, open-world scenarios such as autonomous driving and robotics. However, their uni…
Same Signal, Different Semantics: A Cross-Framework Behavioral Analysis of Software Engineering Agents
Wei Ma, Zhi Chen, Jingxu Gu +3
Behavioral studies of LLM-based software engineering agents extract operational rules about which trajectory shapes correlate with higher resolution rates: that a test step follows…
SpecSyn: LLM-based Synthesis and Refinement of Formal Specifications for Real-world Program Verification
Lezhi Ma, Shangqing Liu, Yi Li +4
Program verification is a formal technique to rigorously ensure the correctness and fault-freeness of software systems. However, constructing comprehensive interprocedural specific…
Defects4C: Benchmarking Large Language Model Repair Capability with C/C++ Bugs
Jian Wang, Xiaofei Xie, Qiang Hu +4
Automated Program Repair (APR) plays a critical role in enhancing the quality and reliability of software systems. While substantial progress has been made in Java-based APR, large…
Do Code Semantics Help? A Comprehensive Study on Execution Trace-Based Information for Code Large Language Models
Jian Wang, Xiaofei Xie, Qiang Hu +2
Code Large Language Models (Code LLMs) have opened a new era in programming with their impressive capabilities. However, recent research has revealed critical limitations in their…
Intention is All You Need: Refining Your Code from Your Intention
Qi Guo, Xiaofei Xie, Shangqing Liu +3
Code refinement aims to enhance existing code by addressing issues, refactoring, and optimizing to improve quality and meet specific requirements. As software projects scale in siz…
Enhancing Code Vulnerability Detection via Vulnerability-Preserving Data Augmentation
Shangqing Liu, Wei Ma, Jian Wang +3
Source code vulnerability detection aims to identify inherent vulnerabilities to safeguard software systems from potential attacks. Many prior studies overlook diverse vulnerabilit…
FT2Ra: A Fine-Tuning-Inspired Approach to Retrieval-Augmented Code Completion
Qi Guo, Xiaohong Li, Xiaofei Xie +6
The rise of code pre-trained models has significantly enhanced various coding tasks, such as code completion, and tools like GitHub Copilot. However, the substantial size of these…
ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairs
Jiaolong Kong, Mingfei Cheng, Xiaofei Xie +3
Automated Program Repair (APR) aims to automatically generate patches for rectifying software bugs. Recent strides in Large Language Models (LLM), such as ChatGPT, have yielded enc…