8 papers
CodeHacker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions
Jingwei Shi, Xinxiang Yin, Jing Huang +2
The evaluation of Large Language Models (LLMs) for code generation relies heavily on the quality and robustness of test cases. However, existing benchmarks often lack coverage for…
Input Reduction Enhanced LLM-based Program Repair
Boyang Yang, Luyao Ren, Xin Yin +3
Large Language Models (LLMs) have shown great potential in Automated Program Repair (APR). Test inputs, being crucial for reasoning the root cause of failures, are always included…
Learning to Align Human Code Preferences
Xin Yin, Chao Ni, Xiaohu Yang
Large Language Models (LLMs) have demonstrated remarkable potential in automating software development tasks. While recent advances leverage Supervised Fine-Tuning (SFT) and Direct…
Navigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Models
Dianshu Liao, Xin Yin, Shidong Pan +3
Unit testing is essential for software quality assurance, yet writing and maintaining tests remains time-consuming and error-prone. To address this challenge, researchers have prop…
Detecting LLM-generated Code with Subtle Modification by Adversarial Training
Xin Yin, Xinrui Li, Chao Ni +2
With the rapid development of Large Language Models (LLMs), their powerful code-generation capabilities have been widely applied in tasks like code completion and automated develop…
Improving the Ability of Pre-trained Language Model by Imparting Large Language Model's Experience
Xin Yin, Chao Ni, Xiaodan Xu +2
Large Language Models (LLMs) and pre-trained Language Models (LMs) have achieved impressive success on many software engineering tasks (e.g., code completion and code generation).…