activity
20212025
most citedBeep: Fine-grained Fix Localization by Learning to Predict Buggy Code Elements

5 citations · 6 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CR2025

Exploring the Security Threats of Knowledge Base Poisoning in Retrieval-Augmented Code Generation

Bo Lin, Shangwen Wang, Liqian Chen +1

The integration of Large Language Models (LLMs) into software development has revolutionized the field, particularly through the use of Retrieval-Augmented Code Generation (RACG) s…

cs.SE2024

There are More Fish in the Sea: Automated Vulnerability Repair via Binary Templates

Bo Lin, Shangwen Wang, Liqian Chen +1

As software vulnerabilities increase in both volume and complexity, vendors often struggle to repair them promptly. Automated vulnerability repair has emerged as a promising soluti…

cs.SE2024

Fault Localization from the Semantic Code Search Perspective

Yihao Qin, Shangwen Wang, Yan Lei +5

The software development process is characterized by an iterative cycle of continuous functionality implementation and debugging, essential for the enhancement of software quality…

cs.SE2023

Enhancing Code Intelligence Tasks with ChatGPT

Kang Yang, Xinjun Mao, Shangwen Wang +6

Pre-trained code models have emerged as crucial tools in various code intelligence tasks. However, their effectiveness depends on the quality of the pre-training dataset, particula…

cs.SE2023★ 1 cited

CCT5: A Code-Change-Oriented Pre-Trained Model

Bo Lin, Shangwen Wang, Zhongxin Liu +3

Software is constantly changing, requiring developers to perform several derived tasks in a timely manner, such as writing a description for the intention of the code change, or id…

cs.SE2021★ 5 cited

Beep: Fine-grained Fix Localization by Learning to Predict Buggy Code Elements

Shangwen Wang, Kui Liu, Bo Lin +4

Software Fault Localization refers to the activity of finding code elements (e.g., statements) that are related to a software failure. The state-of-the-art fault localization techn…