[2603.00004] Bug Severity Prediction in Software Projects Using Supervised Machine Learning Models

Computer Science > Software Engineering arXiv:2603.00004 (cs) [Submitted on 9 Jan 2026] Title:Bug Severity Prediction in Software Projects Using Supervised Machine Learning Models Authors:Nafisha Tamanna Nice View a PDF of the paper titled Bug Severity Prediction in Software Projects Using Supervised Machine Learning Models, by Nafisha Tamanna Nice View PDF Abstract:Bug severity prediction is important in software maintenance, because it helps the development teams to prioritize bugs that have a significant impact on the operation, stability and security of the system. In large software projects bug repositories will grow at very rapid rate making classification of severity manual work labourious and unreliable and prone to human biasness. Many efforts have thus been dedicated on automated ways of severity prediction in the literature of software engineering this http URL study compares different classifiers that are based on supervised machine learning algorithms for predicting bug severity levels using historical repository data from Eclipse Bugzilla. Evaluated methods range from linear classifiers, gradient boosting trees, distance method and transformer-based models, and text features, which are obtained from tokenization, TF-IDF, and n-grams and imbalance correction methods. Models were evaluated in terms of accuracy, precision, recall, F1 score, (AUC-ROC) and confusion matrix. Ensemble tree methods and DistilBERT achieved the top overall accuracy, while linear models...