Deep learning powers faster bug report sorting and assignment
Deep Learning-based Bug Triage System
Software Engineering
Summary
Software teams need to quickly figure out what kind of problems users report and who should fix them. The authors created a system that uses a powerful AI called RoBERTa to read bug reports and decide their category and who should handle them. Their system gets this right 90% of the time after just a bit of training. This shows that these AI models can help automate and speed up software maintenance.
What this means in practice
- •For software development teams: Automatically categorize and assign incoming bug reports to appropriate engineers for faster issue resolution.
- •For customer support centers: Route technical problem reports from users to the right support agents based on automated classification.
Authors
Sourabh Pal
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.