73 citations · 252 across the 54 of their papers we have counts for
20 papers · 1 filter
Defining and Detecting the Defects of the Large Language Model-based Autonomous Agents
Kaiwen Ning, Jiachi Chen, Jingwen Zhang +5
AI agents are systems capable of perceiving their environment, autonomously planning and executing tasks. Recent advancements in LLM have introduced a transformative paradigm for A…
RepoTransBench: A Real-World Multilingual Benchmark for Repository-Level Code Translation
Yanli Wang, Yanlin Wang, Suiquan Wang +8
Repository-level code translation refers to translating an entire code repository from one programming language to another while preserving the functionality of the source reposito…
Definition and Detection of Centralization Defects in Smart Contracts
Zewei Lin, Jiachi Chen, Jiajing Wu +2
In recent years, security incidents stemming from centralization defects in smart contracts have led to substantial financial losses. A centralization defect refers to any error, f…
RMCBench: Benchmarking Large Language Models' Resistance to Malicious Code
Jiachi Chen, Qingyuan Zhong, Yanlin Wang +6
The emergence of Large Language Models (LLMs) has significantly influenced various aspects of software development activities. Despite their benefits, LLMs also pose notable risks,…
Agents in Software Engineering: Survey, Landscape, and Vision
Yanlin Wang, Wanjun Zhong, Yanxian Huang +7
In recent years, Large Language Models (LLMs) have achieved remarkable success and have been widely used in various downstream tasks, especially in the tasks of the software engine…
LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation
Ziyao Zhang, Yanlin Wang, Chong Wang +2
Code generation aims to automatically generate code from input requirements, significantly enhancing development efficiency. Recent large language models (LLMs) based approaches ha…
You Augment Me: Exploring ChatGPT-based Data Augmentation for Semantic Code Search
Yanlin Wang, Lianghong Guo, Ensheng Shi +8
Code search plays a crucial role in software development, enabling developers to retrieve and reuse code using natural language queries. While the performance of code search models…
Hyperion: Unveiling DApp Inconsistencies using LLM and Dataflow-Guided Symbolic Execution
Shuo Yang, Xingwei Lin, Jiachi Chen +5
The rapid advancement of blockchain platforms has significantly accelerated the growth of decentralized applications (DApps). Similar to traditional applications, DApps integrate f…
MCGMark: An Encodable and Robust Online Watermark for Tracing LLM-Generated Malicious Code
Kaiwen Ning, Jiachi Chen, Qingyuan Zhong +8
With the advent of large language models (LLMs), numerous software service providers (SSPs) are dedicated to developing LLMs customized for code generation tasks, such as CodeLlama…
When to Stop? Towards Efficient Code Generation in LLMs with Excess Token Prevention
Lianghong Guo, Yanlin Wang, Ensheng Shi +6
Code generation aims to automatically generate code snippets that meet given natural language requirements and plays an important role in software development. Although Code LLMs h…
RLCoder: Reinforcement Learning for Repository-Level Code Completion
Yanlin Wang, Yanli Wang, Daya Guo +4
Repository-level code completion aims to generate code for unfinished code snippets within the context of a specified repository. Existing approaches mainly rely on retrieval-augme…
Identifying Smart Contract Security Issues in Code Snippets from Stack Overflow
Jiachi Chen, Chong Chen, Jiang Hu +4
Smart contract developers frequently seek solutions to developmental challenges on Q&A platforms such as Stack Overflow (SO). Although community responses often provide viable solu…
Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Models
Yanlin Wang, Tianyue Jiang, Mingwei Liu +5
Large language models (LLMs) have brought a paradigm shift to the field of code generation, offering the potential to enhance the software development process. However, previous re…
SmartOracle: Generating Smart Contract Oracle via Fine-Grained Invariant Detection
Jianzhong Su, Jiachi Chen, Zhiyuan Fang +3
As decentralized applications (DApps) proliferate, the increased complexity and usage of smart contracts have heightened their susceptibility to security incidents and financial lo…
CoSQA+: Pioneering the Multi-Choice Code Search Benchmark with Test-Driven Agents
Jing Gong, Yanghui Wu, Linxi Liang +4
Semantic code search, retrieving code that matches a given natural language query, is an important task to improve productivity in software engineering. Existing code search datase…
Uncover the Premeditated Attacks: Detecting Exploitable Reentrancy Vulnerabilities by Identifying Attacker Contracts
Shuo Yang, Jiachi Chen, Mingyuan Huang +2
Reentrancy, a notorious vulnerability in smart contracts, has led to millions of dollars in financial loss. However, current smart contract vulnerability detection tools suffer fro…
Efficiently Detecting Reentrancy Vulnerabilities in Complex Smart Contracts
Zexu Wang, Jiachi Chen, Yanlin Wang +3
Reentrancy vulnerability as one of the most notorious vulnerabilities, has been a prominent topic in smart contract security research. Research shows that existing vulnerability de…
CRPWarner: Warning the Risk of Contract-related Rug Pull in DeFi Smart Contracts
Zewei Lin, Jiachi Chen, Zibin Zheng +3
In recent years, Decentralized Finance (DeFi) grows rapidly due to the development of blockchain technology and smart contracts. As of March 2023, the estimated global cryptocurren…
Unity is Strength: Enhancing Precision in Reentrancy Vulnerability Detection of Smart Contract Analysis Tools
Zexu Wang, Jiachi Chen, Zibin Zheng +3
Reentrancy is one of the most notorious vulnerabilities in smart contracts, resulting in significant digital asset losses. However, many previous works indicate that current Reentr…
An Empirical Study on Low-Code Programming using Traditional vs Large Language Model Support
Yongkun Liu, Jiachi Chen, Tingting Bi +6
Low-code programming (LCP) refers to programming using models at higher levels of abstraction, resulting in less manual and more efficient programming, and reduced learning effort…