9 citations · 20 across the 13 of their papers we have counts for
16 papers
Taylor-Approximation-Based Repair of High Floating-Point Errors
Xin Yi, Liqian Chen, Hengbiao Yu +5
Numerical libraries can still lose many bits of accuracy on localized ill-conditioned inputs, such as roots, poles, cancellation points, and branch boundaries. This paper presents…
Shapley-Guided Neural Repair Approach via Derivative-Free Optimization
Xinyu Sun, Wanwei Liu, Haoang Chi +7
DNNs are susceptible to defects like backdoors, adversarial attacks, and unfairness, undermining their reliability. Existing approaches mainly involve retraining, optimization, con…
Atomizer: An LLM-based Collaborative Multi-Agent Framework for Intent-Driven Commit Untangling
Kangchen Zhu, Zhiliang Tian, Shangwen Wang +2
Composite commits, which entangle multiple unrelated concerns, are prevalent in software development and significantly hinder program comprehension and maintenance. Existing automa…
Give LLMs a Security Course: Securing Retrieval-Augmented Code Generation via Knowledge Injection
Bo Lin, Shangwen Wang, Yihao Qin +2
Retrieval-Augmented Code Generation (RACG) leverages external knowledge to enhance Large Language Models (LLMs) in code synthesis, improving the functional correctness of the gener…
Smoke and Mirrors: Jailbreaking LLM-based Code Generation via Implicit Malicious Prompts
Sheng Ouyang, Yihao Qin, Bo Lin +3
The proliferation of Large Language Models (LLMs) has revolutionized natural language processing and significantly impacted code generation tasks, enhancing software development ef…
Large Language Models-Aided Program Debloating
Bo Lin, Shangwen Wang, Yihao Qin +2
As software grows in complexity to accommodate diverse features and platforms, software bloating has emerged as a significant challenge, adversely affecting performance and securit…
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…
Keep It Simple: Towards Accurate Vulnerability Detection for Large Code Graphs
Xin Peng, Shangwen Wang, Yihao Qin +3
Software vulnerability detection is crucial for high-quality software development. Recently, some studies utilizing Graph Neural Networks (GNNs) to learn the graph representation o…
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…
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…
AgentFL: Scaling LLM-based Fault Localization to Project-Level Context
Yihao Qin, Shangwen Wang, Yiling Lou +4
Fault Localization (FL) is an essential step during the debugging process. With the strong capabilities of code comprehension, the recent Large Language Models (LLMs) have demonstr…
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…
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…
Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning
Mingyang Geng, Shangwen Wang, Dezun Dong +5
Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a…
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…
Attention Please: Consider Mockito when Evaluating Newly Proposed Automated Program Repair Techniques
Shangwen Wang, Ming Wen, Xiaoguang Mao +1
Automated program repair (APR) has attracted widespread attention in recent years with substantial techniques being proposed. Meanwhile, a number of benchmarks have been establishe…