6 papers
PAFT: Preservation Aware Fine-Tuning for Minimal-Edit Program Repair
Boyang Yang, Zijian Cai, Shunfu Jin +1
Large language models (LLMs) are effective for automated program repair, but plausible patches that pass the full test suite often rewrite more code than necessary, increasing revi…
Beyond Localization: Recoverable Headroom and Residual Frontier in Repository-Level RAG-APR
Pengtao Zhao, Boyang Yang, Bach Le +2
Repository-level automated program repair (APR) increasingly treats stronger localization as the main path to better repair. We ask a more targeted question: once localization is s…
A Taxonomy of Prompt Defects in LLM Systems
Haoye Tian, Chong Wang, BoYang Yang +2
Large Language Models (LLMs) have become key components of modern software, with prompts acting as their de-facto programming interface. However, prompt design remains largely empi…
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…
Unlocking LLM Repair Capabilities Through Cross-Language Translation and Multi-Agent Refinement
Wenqiang Luo, Jacky Wai Keung, Boyang Yang +4
Recent advances in leveraging LLMs for APR have demonstrated impressive capabilities in fixing software defects. However, current LLM-based approaches predominantly focus on mainst…
When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair
Wenqiang Luo, Jacky Wai Keung, Boyang Yang +5
Software systems have been evolving rapidly and inevitably introducing bugs at an increasing rate, leading to significant losses in resources consumed by software maintenance. Rece…