This project explores ML-based approaches for classifying software bugs using Neural Networks, Random Forest, Gradient Boosting, and Logistic Regression. The goal was to improve bug detection efficiency and support software testing teams.
- Data preprocessing (Python)
- Feature engineering
- Supervised ML (RF, GB, LR, NN)
- Classification performance evaluation (Accuracy, Precision, Recall, AUC)
Machine learning models demonstrated improved detection capability over classical/manual testing processes.
Python 路 Scikit-Learn 路 Pandas 路 NumPy 路 Power BI 路 Jupyter
- Notebook
- Data
- Report
- Dashboard (Images)
MSc Dissertation Project | Cardiff Metropolitan University