Publications (23)
ZeroCoder: Can LLMs Improve Code Generation Without Ground-Truth Supervision?
Lishui Fan, Mouxiang Chen, Tingwei Zhu +4
Code generation is important in software engineering, and Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful paradigm to improve it through execution-based feedbac…
On Reliability of Patch Correctness Assessment
Xuan Bach D. Le, Lingfeng Bao, David Lo +2
Current state-of-the-art automatic software repair (ASR) techniques rely heavily on incomplete specifications, e.g., test suites, to generate repairs. This, however, may render ASR…
Less is More: On the Importance of Data Quality for Unit Test Generation
Junwei Zhang, Xing Hu, Shan Gao +3
Unit testing is crucial for software development and maintenance. Effective unit testing ensures and improves software quality, but writing unit tests is time-consuming and labor-i…
Mining Sandboxes for Linux Containers
Zhiyuan Wan, David Lo, Xin Xia +2
A container is a group of processes isolated from other groups via distinct kernel namespaces and resource allocation quota. Attacks against containers often leverage kernel exploi…
Clean Code, Better Models: Enhancing LLM Performance with Smell-Cleaned Dataset
Zhipeng Xue, Xiaoting Zhang, Zhipeng Gao +4
The Large Language Models (LLMs) have demonstrated great potential in code-related tasks. However, most research focuses on improving the output quality of LLMs (e.g., correctness)…
CodeMatcher: Searching Code Based on Sequential Semantics of Important Query Words
Chao Liu, Xin Xia, David Lo +3
To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed ma…