5 papers · 1 filter
LLM-based Vulnerability Detection at Project Scale: An Empirical Study
Fengjie Li, Jiajun Jiang, Dongchi Chen +1
In this paper, we present the first comprehensive empirical study of specialized LLM-based detectors and compare them with traditional static analyzers at the project scale. Specif…
Improving Compiler Bug Isolation by Leveraging Large Language Models
Yixian Qi, Jiajun Jiang, Fengjie Li +3
Compilers play a foundational role in building reliable software systems, and bugs within them can lead to catastrophic consequences. The compilation process typically involves hun…
Empirical Evaluation of Large Language Models in Automated Program Repair
Jiajun Sun, Fengjie Li, Xinzhu Qi +2
The increasing prevalence of software bugs has made automated program repair (APR) a key research focus. Large language models (LLMs) offer new opportunities for APR, but existing…
Evaluating the Generalizability of LLMs in Automated Program Repair
Fengjie Li, Jiajun Jiang, Jiajun Sun +1
LLM-based automated program repair methods have attracted significant attention for their state-of-the-art performance. However, they were primarily evaluated on a few well known d…
Hybrid Automated Program Repair by Combining Large Language Models and Program Analysis
Fengjie Li, Jiajun Jiang, Jiajun Sun +1
Automated Program Repair (APR) has garnered significant attention due to its potential to streamline the bug repair process for human developers. Recently, LLM-based APR methods ha…