8 citations · 31 across the 8 of their papers we have counts for
8 papers
TIGER: A Generating-Then-Ranking Framework for Practical Python Type Inference
Chong Wang, Jian Zhang, Yiling Lou +4
Python's dynamic typing system offers flexibility and expressiveness but can lead to type-related errors, prompting the need for automated type inference to enhance type hinting. W…
LLMs Meet Library Evolution: Evaluating Deprecated API Usage in LLM-based Code Completion
Chong Wang, Kaifeng Huang, Jian Zhang +4
Large language models (LLMs), pre-trained or fine-tuned on large code corpora, have shown effectiveness in generating code completions. However, in LLM-based code completion, LLMs…
Efficient Universal Goal Hijacking with Semantics-guided Prompt Organization
Yihao Huang, Chong Wang, Xiaojun Jia +5
Universal goal hijacking is a kind of prompt injection attack that forces LLMs to return a target malicious response for arbitrary normal user prompts. The previous methods achieve…
Evaluating Large Language Models for Line-Level Vulnerability Localization
Jian Zhang, Chong Wang, Anran Li +4
Recently, Automated Vulnerability Localization (AVL) has attracted growing attention, aiming to facilitate diagnosis by pinpointing the specific lines of code responsible for vulne…
BadEdit: Backdooring large language models by model editing
Yanzhou Li, Tianlin Li, Kangjie Chen +5
Mainstream backdoor attack methods typically demand substantial tuning data for poisoning, limiting their practicality and potentially degrading the overall performance when applie…
Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications
Wei Ma, Daoyuan Wu, Yuqiang Sun +5
Smart contracts are decentralized applications built atop blockchains like Ethereum. Recent research has shown that large language models (LLMs) have potential in auditing smart co…
Teaching Code LLMs to Use Autocompletion Tools in Repository-Level Code Generation
Chong Wang, Jian Zhang, Yebo Feng +4
Code large language models (LLMs) face limitations in repository-level code generation due to their lack of awareness of repository-level dependencies (e.g., user-defined attribute…
A Prompt Learning Framework for Source Code Summarization
Tingting Xu, Yun Miao, Chunrong Fang +8
(Source) code summarization is the task of automatically generating natural language summaries (also called comments) for given code snippets. Recently, with the successful applica…