Automated Bug Report Classification Using BERT: A Transformer-Based Approach for Efficient Bug Triage in Large-Scale Software Projects

Efficient bug triage is a critical aspect of large-scale software development, yet it remains a labor-intensive and error-prone task. This paper presents a novel approach to automated bug report classification by leveraging BERT, a state-of-the-art transformer-based LLM, to categorize bug reports into well-defined software defect types. We introduce a structured and scalable classification taxonomy designed to reflect the complexities of real-world bug reports. The proposed method incorporates fine-tuning of BERT on domain-specific datasets and evaluates performance across multiple bug categories using accuracy, precision, recall, and F1-score metrics. Empirical results demonstrate that our approach outperforms traditional machine learning methods, achieving an overall accuracy of 72% and delivering particularly strong performance in critical categories such as security and performance bugs. Using both the bug title and description as input produced the best results, underscoring the importance of contextual detail in effective bug triage. This work contributes to the field of software defect classification by providing a replicable and adaptable methodology for automated bug triage, with practical implications for enhancing software maintenance and quality in large-scale development projects.