5 papers · 1 filter
Exploring Large Language Models in Resolving Environment-Related Crash Bugs: Localizing and Repairing
Xueying Du, Mingwei Liu, Hanlin Wang +3
Software crash bugs cause unexpected program behaviors or even abrupt termination, thus demanding immediate resolution. However, resolving crash bugs can be challenging due to thei…
Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG
Xueying Du, Geng Zheng, Kaixin Wang +9
Although LLMs have shown promising potential in vulnerability detection, this study reveals their limitations in distinguishing between vulnerable and similar-but-benign patched co…
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…
STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis
Junwei Liu, Yixuan Chen, Mingwei Liu +2
Repository-level code completion is challenging as it involves complicated contexts from multiple files in the repository. To date, researchers have proposed two technical categori…
No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation
Zhiqiang Yuan, Yiling Lou, Mingwei Liu +4
Unit testing is essential in detecting bugs in functionally-discrete program units. Manually writing high-quality unit tests is time-consuming and laborious. Although traditional t…