55 citations · 122 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…
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…
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…
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…
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…
ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation
Xueying Du, Mingwei Liu, Kaixin Wang +7
In this work, we make the first attempt to evaluate LLMs in a more challenging code generation scenario, i.e. class-level code generation. We first manually construct the first cla…
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…