Deep Learning-based Bug Triage System

Abstract

Effective bug triage is crucial for streamlining the software development lifecycle by accurately categorizing and assigning reported software defects. In this paper, we propose an automated bug triage system built upon the pre-trained RoBERTa-base transformer architecture. By leveraging deep contextual representations, our approach efficiently classifies incoming bug reports to optimize assignment. Experimental evaluation demonstrates that the proposed system achieves a strong bug identification accuracy of 0.90 within just five training epochs. These findings highlight the efficiency and high performance of fine-tuned transformer models for practical software engineering automation.