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AdaRepair-Mem: Adaptive Experience Orchestration for Repository-Level Program Repair
Z. C. Luo, J. C. Guo, W. J. He +10
Recent memory-augmented repository-level program repair methods reuse historical repair experiences to improve LLM-based issue resolution. However, our analysis reveals three limit…
Weaponizing the Commons: A Taxonomy and Detection Framework of Abuse on GitHub
Yuli Cheng, Xiaoyu Zhang, Jiongchi Yu +3
GitHub plays a critical role in modern software supply chains, making its security an important research concern. Existing studies have primarily focused on CI/CD automation, colla…
Human in the Loop for Fuzz Testing: Literature Review and the Road Ahead
Jiongchi Yu, Xiaolin Wen, Sizhe Cheng +3
Fuzz testing is one of the most effective techniques for detecting bugs and vulnerabilities in software. However, as the basis of fuzz testing, automated heuristics often fail to u…
Defects4C: Benchmarking Large Language Model Repair Capability with C/C++ Bugs
Jian Wang, Xiaofei Xie, Qiang Hu +4
Automated Program Repair (APR) plays a critical role in enhancing the quality and reliability of software systems. While substantial progress has been made in Java-based APR, large…
AutoEmpirical: LLM-Based Automated Research for Empirical Software Fault Analysis
Jiongchi Yu, Weipeng Jiang, Xiaoyu Zhang +3
Understanding software faults is essential for empirical research in software development and maintenance. However, traditional fault analysis, while valuable, typically involves m…
Leveraging LLM Agents for Automated Video Game Testing
Chengjia Wang, Lanling Tang, Ming Yuan +3
Testing MMORPGs (Massively Multiplayer Online Role-Playing Games) is a critical yet labor-intensive task in game development due to their complexity and frequent updating nature. T…
A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code
Keke Lian, Bin Wang, Lei Zhang +19
The increasing adoption of large language models (LLMs) in software engineering necessitates rigorous security evaluation of their generated code. However, existing benchmarks ofte…
Understanding the Supply Chain and Risks of Large Language Model Applications
Yujie Ma, Lili Quan, Xiaofei Xie +4
The rise of Large Language Models (LLMs) has led to the widespread deployment of LLM-based systems across diverse domains. As these systems proliferate, understanding the risks ass…
The Foundation Cracks: A Comprehensive Study on Bugs and Testing Practices in LLM Libraries
Weipeng Jiang, Xiaoyu Zhang, Xiaofei Xie +4
Large Language Model (LLM) libraries have emerged as the foundational infrastructure powering today's AI revolution, serving as the backbone for LLM deployment, inference optimizat…