9 citations · 16 across the 21 of their papers we have counts for
22 papers
Mitigating Package Hallucinations in Large Language Models via Model Editing
Shuhan Liu, Yukai Zhao, Xing Hu +3
Large language models (LLMs) have demonstrated strong capabilities in software engineering tasks, such as code generation, library recommendation, and dependency configuration. How…
Refploit: Facilitating Exploit Construction via Code-Agent Trajectory Repair
Zirui Chen, Zhipeng Xue, Jiayuan Zhou +3
Vulnerability exploits play a crucial role in assessing the downstream impact of Java library vulnerabilities. While some vulnerabilities are accompanied by disclosed exploit refer…
IntentTester: Intent-Driven Multi-agent Framework for Cross-Library Test Migration
Yi Gao, Ziyuan Zhang, Xing Hu +2
Unit tests capture both functional checks and domain-specific knowledge, but this knowledge remains locked within individual projects and is rarely reused across libraries with ove…
RACE-Bench: A Reasoning-Augmented Benchmark for Repository-Level Code Agents on Feature Addition
Shuhan Liu, Zhiyi Zhao, Xing Hu +3
Repository-level code agents have shown strong promise in real-world feature addition tasks, making reliable evaluation of their capabilities increasingly important. However, exist…
Assessing the Cross-Version Applicability of Java Library Vulnerability Exploits
Zirui Chen, Qi Zhan, Jiayuan Zhou +3
Open-source software supply chain security relies heavily on assessing affected versions of library vulnerabilities. While prior studies have leveraged exploits for verifying vulne…
Every Maintenance Has Its Exemplar: The Future of Software Maintenance through Migration
Zirui Chen, Xing Hu, Xin Xia +1
Maintenance is a critical stage in the software lifecycle, ensuring that post-release systems remain reliable, efficient, and adaptable. However, manual software maintenance is lab…
DepRadar: Agentic Coordination for Context Aware Defect Impact Analysis in Deep Learning Libraries
Yi Gao, Xing Hu, Tongtong Xu +3
Deep learning libraries like Transformers and Megatron are now widely adopted in modern AI programs. However, when these libraries introduce defects, ranging from silent computatio…
Diffploit: Facilitating Cross-Version Exploit Migration for Open Source Library Vulnerabilities
Zirui Chen, Zhipeng Xue, Jiayuan Zhou +3
Exploits are commonly used to demonstrate the presence of library vulnerabilities and validate their impact across different versions. However, their direct application to alternat…
An Empirical Study of Vulnerable Package Dependencies in LLM Repositories
Shuhan Liu, Xing Hu, Xin Xia +2
Large language models (LLMs) have developed rapidly in recent years, revolutionizing various fields. Despite their widespread success, LLMs heavily rely on external code dependenci…
CREME: Robustness Enhancement of Code LLMs via Layer-Aware Model Editing
Shuhan Liu, Xing Hu, Kerui Huang +3
Large language models (LLMs) have demonstrated impressive capabilities in code generation, where the natural language prompt plays a crucial role in conveying user intent to the mo…
Re-Evaluating Code LLM Benchmarks Under Semantic Mutation
Zhiyuan Pan, Xing Hu, Xin Xia +1
In the era of large language models (LLMs), code benchmarks have become an important research area in software engineering and are widely used by practitioners. These benchmarks ev…
Generating Mitigations for Downstream Projects to Neutralize Upstream Library Vulnerability
Zirui Chen, Xing Hu, Puhua Sun +2
Third-party libraries are essential in software development as they prevent the need for developers to recreate existing functionalities. However, vulnerabilities within these libr…
What Makes a Good TODO Comment?
Haoye Wang, Zhipeng Gao, Tingting Bi +4
Software development is a collaborative process that involves various interactions among individuals and teams. TODO comments in source code play a critical role in managing and co…
Instructive Code Retriever: Learn from Large Language Model's Feedback for Code Intelligence Tasks
Jiawei Lu, Haoye Wang, Zhongxin Liu +3
Recent studies proposed to leverage large language models (LLMs) with In-Context Learning (ICL) to handle code intelligence tasks without fine-tuning. ICL employs task instructions…
Automated Unit Test Refactoring
Yi Gao, Xing Hu, Xiaohu Yang +1
Test smells arise from poor design practices and insufficient domain knowledge, which can lower the quality of test code and make it harder to maintain and update. Manually refacto…
Vulnerability-Triggering Test Case Generation from Third-Party Libraries
Yi Gao, Xing Hu, Zirui Chen +1
Open-source third-party libraries are widely used in software development. These libraries offer substantial advantages in terms of time and resource savings. However, a significan…
GUIMigrator: Semantics-Preserving Transpilation from Android XML to Compose and SwiftUI
Yi Gao, Xing Hu, Xin Xia +1
Constructing user interfaces (UIs) is one of the most resource-intensive tasks in mobile development, often consuming more than half of overall effort. Although declarative framewo…
CATCODER: Repository-Level Code Generation with Relevant Code and Type Context
Zhiyuan Pan, Xing Hu, Xin Xia +1
Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, repository-level code generation presents unique challenges, particularly…
Pre-training by Predicting Program Dependencies for Vulnerability Analysis Tasks
Zhongxin Liu, Zhijie Tang, Junwei Zhang +2
Vulnerability analysis is crucial for software security. This work focuses on using pre-training techniques to enhance the understanding of vulnerable code and boost vulnerability…
PPT4J: Patch Presence Test for Java Binaries
Zhiyuan Pan, Xing Hu, Xin Xia +3
The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from…
Exploiting Library Vulnerability via Migration Based Automating Test Generation
Zirui Chen, Xing Hu, Xin Xia +4
In software development, developers extensively utilize third-party libraries to avoid implementing existing functionalities. When a new third-party library vulnerability is disclo…
From Misuse to Mastery: Enhancing Code Generation with Knowledge-Driven AI Chaining
Xiaoxue Ren, Xinyuan Ye, Dehai Zhao +2
Large Language Models (LLMs) have shown promising results in automatic code generation by improving coding efficiency to a certain extent. However, generating high-quality and reli…