5 papers
Ensembling Large Language Models for Code Vulnerability Detection: An Empirical Evaluation
Zhihong Sun, Jia Li, Yao Wan +7
Code vulnerability detection is crucial for ensuring the security and reliability of modern software systems. Recently, Large Language Models (LLMs) have shown promising capabiliti…
Empirical Study of Code Large Language Models for Binary Security Patch Detection
Qingyuan Li, Binchang Li, Cuiyun Gao +2
Security patch detection (SPD) is crucial for maintaining software security, as unpatched vulnerabilities can lead to severe security risks. In recent years, numerous learning-base…
Refactoring Bug-Inducing: Improving Defect Prediction with Code Change Tactics Analysis
Feifei Niu, Junqian Shao, Christoph Mayr-Dorn +5
Just-in-time defect prediction (JIT-DP) aims to predict the likelihood of code changes resulting in software defects at an early stage. Although code change metrics and semantic fe…
Enhancement Report Approval Prediction: A Comparative Study of Large Language Models
Haosheng Zuo, Feifei Niu, Chuanyi Li
Enhancement reports (ERs) serve as a critical communication channel between users and developers, capturing valuable suggestions for software improvement. However, manually process…
When Deep Learning Meets Information Retrieval-based Bug Localization: A Survey
Feifei Niu, Chuanyi Li, Kui Liu +2
Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. Information retrieval-based bug localization (IRBL) identifies buggy co…