4 citations · 19 across the 13 of their papers we have counts for
7 papers · 1 filter
LLM Based Input Space Partitioning Testing for Library APIs
Jiageng Li, Zhen Dong, Chong Wang +4
Automated library APIs testing is difficult as it requires exploring a vast space of parameter inputs that may involve objects with complex data types. Existing search based approa…
Towards Trustworthy LLMs for Code: A Data-Centric Synergistic Auditing Framework
Chong Wang, Zhenpeng Chen, Tianlin Li +2
LLM-powered coding and development assistants have become prevalent to programmers' workflows. However, concerns about the trustworthiness of LLMs for code persist despite their wi…
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…
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…