6 papers
On the Effectiveness of Code Representation in Deep Learning-Based Automated Patch Correctness Assessment
Quanjun Zhang, Haichuan Hu, Chunrong Fang +5
Automated program repair (APR) attempts to generate correct patches and has drawn wide attention from both academia and industry in the past decades. However, APR is continuously s…
Red Skills or Blue Skills? A Dive Into Skills Published on ClawHub
Haichuan Hu, Ye Shang, Quanjun Zhang
Skill ecosystems have emerged as an increasingly important layer in Large Language Model (LLM) agent systems, enabling reusable task packaging, public distribution, and community-d…
CL4SE: Benchmarking Context Learning on Software Engineering
Haichuan Hu, Quanjun Zhang, Ye Shang +4
Context engineering has emerged as a pivotal paradigm for unlocking the potential of Large Language Models (LLMs) in Software Engineering (SE) tasks, enabling performance gains at…
ComPass: Contrastive Learning for Automated Patch Correctness Assessment in Program Repair
Quanjun Zhang, Ye Shang, Haichuan Hu +3
Automated program repair (APR) attempts to reduce manual debugging efforts and plays a vital role in software maintenance. Despite remarkable progress, APR is still limited in gene…
Repair-R1: Better Test Before Repair
Haichuan Hu, Xiaochen Xie, Quanjun Zhang
APR (Automated Program Repair) aims to automatically locate program defects, generate patches and validate the repairs. Existing techniques for APR are often combined with LLMs (La…
Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs
Haichuan Hu, Ye Shang, Guolin Xu +2
LLMs have long demonstrated remarkable effectiveness in automatic program repair (APR), with OpenAI's ChatGPT being one of the most widely used models in this domain. Through conti…