4 citations · 11 across the 7 of their papers we have counts for
5 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…
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…