5 papers
OmniLLP: Enhancing LLM-based Log Level Prediction with Context-Aware Retrieval
Youssef Esseddiq Ouatiti, Mohammed Sayagh, Bram Adams +1
Developers insert logging statements in source code to capture relevant runtime information essential for maintenance and debugging activities. Log level choice is an integral, yet…
An ML-based Approach to Predicting Software Change Dependencies: Insights from an Empirical Study on OpenStack
Ali Arabat, Mohammed Sayagh, Jameleddine Hassine
As software systems grow in complexity, accurately identifying and managing dependencies among changes becomes increasingly critical. For instance, a change that leverages a functi…
Think Broad, Act Narrow: CWE Identification with Multi-Agent Large Language Models
Mohammed Sayagh, Mohammad Ghafari
Machine learning and Large language models (LLMs) for vulnerability detection has received significant attention in recent years. Unfortunately, state-of-the-art techniques show th…
Towards Conversational Development Environments: Using Theory-of-Mind and Multi-Agent Architectures for Requirements Refinement
Keheliya Gallaba, Ali Arabat, Dayi Lin +2
Foundation Models (FMs) have shown remarkable capabilities in various natural language tasks. However, their ability to accurately capture stakeholder requirements remains a signif…
Security Bug Report Prediction Within and Across Projects: A Comparative Study of BERT and Random Forest
Farnaz Soltaniani, Mohammad Ghafari, Mohammed Sayagh
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…