Intelligent Bug Triaging Using CodeBERT, Graph Attention Networks, FAISS, and Explainable Machine Learning
TL;DR
By combining semantic understanding, graph learning, similarity retrieval, and explainable artificial intelligence, the proposed system improves the efficiency, accuracy, and interpretability of automated bug triaging, making it suitable for large-scale software maintenance environments.
Abstract
Software bug triaging is a crucial software maintenance activity that involves assigning reported bugs to appropriate developers and determining their priority. Manual bug triaging is often time-consuming, error-prone, and inefficient for largescale software projects due to the increasing volume of bug reports. This paper presents an intelligent bug triaging framework that integrates CodeBERT, Graph Attention Networks (GAT), FAISS, XGBoost, and SHAP to automate bug analysis and improve decision-making. Initially, bug reports are preprocessed and transformed into contextual semantic embeddings using CodeBERT. FAISS performs efficient similarity search to retrieve related historical bug reports, while GAT captures relationships among bug reports, developers, and software components to enhance developer recommendation. The extracted semantic and graph-based features are combined and supplied to an XGBoost classifier for bug priority prediction. To improve model transparency, SHAP is employed to explain the contribution of individual features to each prediction. The proposed framework is deployed through a Streamlit-based web application that provides an interactive interface for bug analysis and recommendation. By combining semantic understanding, graph learning, similarity retrieval, and explainable artificial intelligence, the proposed system improves the efficiency, accuracy, and interpretability of automated bug triaging, making it suitable for large-scale software maintenance environments.