121 citations · 173 across the 25 of their papers we have counts for
24 papers · 1 filter
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…
Behavior Specification-Guided Program Synthesis for Binary Deobfuscation
Kangchen Zhu, Shangwen Wang, Zhiliang Tian +5
Deobfuscation is critical to reverse engineering and security analysis because it restores the readability and analyzability of obfuscated code. However, existing research primaril…
Three Heads Are Better Than One: A Multi-perspective Reasoning Framework for Enhanced Vulnerability Detection
Xin Peng, Bo Lin, Jing Wang +5
Automated vulnerability detection is crucial for enhancing software security by identifying potential flaws that attackers could exploit, thereby reducing the reliance on labor-int…
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…
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…
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…
Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering
Tanghaoran Zhang, Yue Yu, Xinjun Mao +5
Code snippet adaptation is a fundamental activity in the software development process. Unlike code generation, code snippet adaptation is not a "free creation", which requires deve…
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…
When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference
Zhensu Sun, Xiaoning Du, Fu Song +2
Leveraging recent advancements in large language models, modern neural code completion models have demonstrated the capability to generate highly accurate code suggestions. However…
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…
Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems
Zhensu Sun, Xiaoning Du, Fu Song +4
Currently, large pre-trained language models are widely applied in neural code completion systems. Though large code models significantly outperform their smaller counterparts, aro…
Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
Haoye Tian, Xunzhu Tang, Andrew Habib +5
In this work, we propose a novel perspective to the problem of patch correctness assessment: a correct patch implements changes that "answer" to a problem posed by buggy behaviour.…
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…
A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits
Steffen Herbold, Alexander Trautsch, Benjamin Ledel +45
Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only…
On the Efficiency of Test Suite based Program Repair: A Systematic Assessment of 16 Automated Repair Systems for Java Programs
Kui Liu, Shangwen Wang, Anil Koyuncu +7
Test-based automated program repair has been a prolific field of research in software engineering in the last decade. Many approaches have indeed been proposed, which leverage test…
How Different Is It Between Machine-Generated and Developer-Provided Patches? An Empirical Study on The Correct Patches Generated by Automated Program Repair Techniques
Shangwen Wang, Ming Wen, Liqian Chen +2
Background: Over the years, Automated Program Repair (APR) has attracted much attention from both academia and industry since it can reduce the costs in fixing bugs. However, how t…
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…
An Initial Step Towards Organ Transplantation Based on GitHub Repository
Shangwen Wang, Xiaoguang Mao, Yue Yu
Organ transplantation, which is the utilization of codes directly related to some specific functionalities to complete ones own program, provides more convenience for developers th…
Multi-Location Program Repair Strategies Learned from Past Successful Experience
Shangwen Wang, Xiaoguang Mao, Nan Niu +2
Automated program repair (APR) has great potential to reduce the effort and time-consumption in software maintenance and becomes a hot topic in software engineering recently with m…