Bug Classification of Testing Data Using Machine Learning

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.

🔍 Techniques

• Data preprocessing (Python)
• Feature engineering
• Supervised ML (RF, GB, LR, NN)
• Classification performance evaluation (Accuracy, Precision, Recall, AUC)

📊 Results

Machine learning models demonstrated improved detection capability over classical/manual testing processes.

🛠 Tools

Python · Scikit-Learn · Pandas · NumPy · Power BI · Jupyter

📂 Files

• Notebook
• Data
• Report
• Dashboard (Images)

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🎓 Context

MSc Dissertation Project | Cardiff Metropolitan University