6 citations · 13 across the 5 of their papers we have counts for
5 papers
Exploring Multi-Lingual Bias of Large Code Models in Code Generation
Chaozheng Wang, Zongjie Li, Cuiyun Gao +6
Code generation aims to synthesize code and fulfill functional requirements based on natural language (NL) specifications, which can greatly improve development efficiency. In the…
Testing and Understanding Erroneous Planning in LLM Agents through Synthesized User Inputs
Zhenlan Ji, Daoyuan Wu, Pingchuan Ma +2
Agents based on large language models (LLMs) have demonstrated effectiveness in solving a wide range of tasks by integrating LLMs with key modules such as planning, memory, and too…
Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach
Zhenlan Ji, Pingchuan Ma, Zongjie Li +1
While code generation has been widely used in various software development scenarios, the quality of the generated code is not guaranteed. This has been a particular concern in the…
REEF: A Framework for Collecting Real-World Vulnerabilities and Fixes
Chaozheng Wang, Zongjie Li, Yun Peng +5
Software plays a crucial role in our daily lives, and therefore the quality and security of software systems have become increasingly important. However, vulnerabilities in softwar…
"Oops, Did I Just Say That?" Testing and Repairing Unethical Suggestions of Large Language Models with Suggest-Critique-Reflect Process
Pingchuan Ma, Zongjie Li, Ao Sun +1
As the popularity of large language models (LLMs) soars across various applications, ensuring their alignment with human values has become a paramount concern. In particular, given…