55 citations · 100 across the 4 of their papers we have counts for
4 papers
Large Language Model-Based Agents for Software Engineering: A Survey
Junwei Liu, Kaixin Wang, Yixuan Chen +4
The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially exten…
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…
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…
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…