4 citations · 9 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Enhancing LLM's Ability to Generate More Repository-Aware Unit Tests Through Precise Contextual Information Injection
Xin Yin, Chao Ni, Xinrui Li +3
Though many learning-based approaches have been proposed for unit test generation and achieved remarkable performance, they still have limitations in relying on task-specific datas…