12 citations · 50 across the 18 of their papers we have counts for
7 papers · 1 filter
A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Code Smell Detection
Beiqi Zhang, Peng Liang, Xin Zhou +5
Automated code smell detection faces persistent challenges due to the subjectivity of heuristic rules and the limited performance of traditional ML/DL models. While Large Language…
Comparison of Static Application Security Testing Tools and Large Language Models for Repo-level Vulnerability Detection
Xin Zhou, Duc-Manh Tran, Thanh Le-Cong +5
Software vulnerabilities pose significant security challenges and potential risks to society, necessitating extensive efforts in automated vulnerability detection. There are two po…
Large Language Model for Vulnerability Detection and Repair: Literature Review and the Road Ahead
Xin Zhou, Sicong Cao, Xiaobing Sun +1
The significant advancements in Large Language Models (LLMs) have resulted in their widespread adoption across various tasks within Software Engineering (SE), including vulnerabili…
Bridging Expert Knowledge with Deep Learning Techniques for Just-In-Time Defect Prediction
Xin Zhou, DongGyun Han, David Lo
Just-In-Time (JIT) defect prediction aims to automatically predict whether a commit is defective or not, and has been widely studied in recent years. In general, most studies can b…
CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences
Martin Weyssow, Aton Kamanda, Xin Zhou +1
Evaluating the alignment of large language models (LLMs) with user-defined coding preferences is a challenging endeavour that requires a deep assessment of LLMs' outputs. Existing…
Large Language Model for Vulnerability Detection: Emerging Results and Future Directions
Xin Zhou, Ting Zhang, David Lo
Previous learning-based vulnerability detection methods relied on either medium-sized pre-trained models or smaller neural networks from scratch. Recent advancements in Large Pre-T…
Multi-LLM Collaboration + Data-Centric Innovation = 2x Better Vulnerability Repair
Xin Zhou, Kisub Kim, Bowen Xu +2
The advances of deep learning (DL) have paved the way for automatic software vulnerability repair approaches, which effectively learn the mapping from the vulnerable code to the fi…