Evaluating Large Language Models for Security Bug Report Prediction

Early detection of security bug reports SBRs is critical for timely vulnerability mitigation. We present an evaluation of prompt-based engineering and fine-tuning approaches for predicting SBRs using Large Language Models LLMs. Our findings reveal a distinct trade-off between the two approaches...

Few-Shot Learning for Security Bug Report Identification

Security bug reports require prompt identification to minimize the window of vulnerability in software systems. Traditional machine learning ML techniques for classifying bug reports to identify security bug reports rely heavily on large amounts of labeled data. However, datasets for security bug...

Security Bug Report Prediction within and across Projects: a Comparative Study of BERT and Random Forest

Early detection of security bug reports SBRs is crucial for preventing vulnerabilities and ensuring system reliability. While machine learning models have been developed for SBR prediction, their predictive performance still has room for improvement. In this study, we conduct a comprehensive...