7 papers
An Extensive Empirical Study on Code Translation Technique
Ruihang Fan, Jiajun Jiang, Xinpeng Wang +3
Automated code translation is increasingly important for software evolution, yet the relative strengths and limitations of learning-based and large language model (LLM)-based techn…
LLM-based Vulnerability Detection at Project Scale: An Empirical Study
Fengjie Li, Jiajun Jiang, Dongchi Chen +1
As software complexity grows, automated vulnerability detection becomes increasingly important. Recent LLM-based detectors combine semantic reasoning with static analysis for proje…
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…
Boosting Redundancy-based Automated Program Repair by Fine-grained Pattern Mining
Jiajun Jiang, Fengjie Li, Zijie Zhao +5
Redundancy-based automated program repair (APR), which generates patches by referencing existing source code, has gained much attention since they are effective in repairing real-w…