55 citations · 114 across the 12 of their papers we have counts for
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…
Boosting Static Resource Leak Detection via LLM-based Resource-Oriented Intention Inference
Chong Wang, Jianan Liu, Xin Peng +2
Resource leaks, caused by resources not being released after acquisition, often lead to performance issues and system crashes. Existing static detection techniques rely on mechanic…
Recommending Analogical APIs via Knowledge Graph Embedding
Mingwei Liu, Yanjun Yang, Yiling Lou +4
Library migration, which re-implements the same software behavior by using a different library instead of using the current one, has been widely observed in software evolution. One…
Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation
Zhiqiang Yuan, Junwei Liu, Qiancheng Zi +3
In this work, we evaluate 10 open-source instructed LLMs on four representative code comprehension and generation tasks. We have the following main findings. First, for the zero-sh…
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…