Abstract :

Bug Triaging—the Process Of Classifying And Assigning Software Issues To Appropriate Developers— Is A Critical Yet Challenging Task In Large-scale Software Development. Manual Triaging Is Time Consuming, Inconsistent, And Prone To Human Bias, Which Often Delays Issue Resolution And Misallocates Developer Resources. This Study Explores The Application Of Machine Learning To Automate And Improve Bug Triaging Efficiency And Accuracy. Using A Dataset Of Over 122,000 Issues From The Microsoft/vscode GitHub Repository, We Evaluate Several Machine Learning Models Including CNN, Random Forest, And Multinomial Naive Bayes. Our Primary Contribution Is The Development Of An Augmented Bidirectional ML Model That Integrates Enriched Textual Features And Contextual Metadata. This Model, Optimized Using Optuna, Outperforms Traditional Baselines, Achieving A Micro F1-score Of 0.6469 And Hamming Loss Of 0.0133 For Label Prediction, And A Micro F1-score Of 0.5974 With Hamming Loss Of 0.0062 For Assignee Recommendation. In Addition To Demonstrating Strong Predictive Performance, We Present A Robust End-to-end Pipeline For Data Preprocessing, Augmentation, Model Training, And Evaluation Using Multi-label Classification Techniques. The Study Highlights How Deep Learning Architectures, In Combination With Feature Engineering And Hyper Parameter Tuning, Can Provide Scalable And Generalizable Components To Support The Automation Of Bug Triaging. These Findings Contribute To The Growing Field Of Intelligent Software Maintenance By Offering Datadriven Approaches That Can Support Developer Workflows And Improve Issue Management Efficiency In Open-source Environments.

Published:

21-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1037-1041

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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