Optimizing Issue Tracking Systems using Deep Learning-based Issue Classification

This research paper aims to investigate the use of text classification for automatic issue tagging in issue-tracking systems. The study focuses on the current state of issue-tracking systems and their limitations in terms of issue tagging, specifically the manual effort required to tag and categorize issues. The research describes the implementation of a text classification model for automatic issue tagging and evaluates its performance in terms of accuracy and loss. The results of this study show that the use of text classification can significantly improve the efficiency and accuracy of issue tagging in issue-tracking systems, while also providing a more efficient and user-friendly experience. The results also provide valuable insights into the design and implementation of issue-tracking systems and demonstrates the potential of deep learning to enhance the accuracy and efficiency of issue-tracking. This research also provides insights for software development teams and managers on how to use text classification techniques to improve the efficiency and effectiveness of their issue-tracking systems.