Advanced AI system for intelligent bug type prediction with 94.34% accuracy
Develop an intelligent bug classification system that automatically categorizes bug reports into Defects, Tasks, and Enhancements using advanced machine learning techniques.
Combines state-of-the-art ensemble methods, advanced text processing, and proper class balancing to achieve exceptional performance in automated bug classification.
| Bug ID | Type | Summary |
|---|---|---|
| 1949668 | defect |
Race condition between clearing mIsDeferredPurgePending...
|
| 1948993 | task |
Remove Nightly condition for vertical tabs checkboxes...
|
| 1947536 | defect |
QR code image is not exposed to assistive technology...
|
| 1947606 | task |
Add Nimbus support for calculator
|
Removed irrelevant columns and handled missing values
Extracted temporal features and grouped categories
Applied TF-IDF vectorization to bug summaries
Used SMOTE to balance class distribution
Ensemble Champion
Tree Ensemble
Interpretable Model
| Model | Accuracy | Precision | Recall | F1-Score |
|---|---|---|---|---|
| Stacking Classifier | 94.34% | 94.2% | 94.1% | 94.15% |
| Random Forest | 91.73% | 91.5% | 91.8% | 91.65% |
| Decision Tree | 83.81% | 83.5% | 83.9% | 83.70% |
Stacking Classifier achieved 94.34% accuracy by combining multiple algorithms, demonstrating the power of ensemble learning.
TF-IDF vectorization of bug summaries provided the most predictive power with distinctive language patterns.
SMOTE dramatically improved performance by addressing severe class imbalance in the dataset.
Feature engineering and preprocessing significantly boosted model performance and generalization.