# Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory-Attention Mechanism
## 4 Experimental Result
### Results  
#### 4.5.1 Result of Our Approach  
The proposed Priority-Sensitive LSTM-Attention mechanism was evaluated using datasets from the Eclipse and Mozilla open-source projects to assess its predictive performance for bug report priorities. The results are presented and analyzed based on various configurations and parameters to show the effectiveness of the model.  
The performance of the model across both datasets is summarized in Figure 6, where the x-axis represents priority levels and the y-axis denotes prediction accuracy.  
On average, the model achieved an accuracy of 84.11% for Eclipse and 93.00% for Mozilla. These results indicate that the proposed model is capable of accurately predicting the priority of bug reports, reflecting its potential for practical application in software development workflows.  
Further analysis was conducted to evaluate the impact of varying the number of LSTM layers on model performance, as shown in Figure 7.  
Figure 6: Performance of proposed model.  
For the Eclipse dataset, the best performance, with an accuracy of 84.39%, was achieved using a single layer. Conversely, for the Mozilla dataset, the model exhibited its highest performance with five layers. These results suggest that the optimal configuration of LSTM layers may depend on the characteristics of the dataset, such as its size and complexity. Consequently, the model was configured with one layer for Eclipse and five layers for Mozilla to maximize the predictive accuracy of the model for each dataset.  
The effect of dropout rates on model performance was also examined to address overfitting concerns, as illustrated in Figures 8 and 9.  
For the Eclipse dataset, the optimal dropout rate was determined to be 0.2, resulting in a Precision of 93.50%, Recall of 88.18%, and F-measure of 90.14%. The best accuracy achieved was 92.17%, representing an improvement of 2.03% compared to the average accuracy. For the Mozilla dataset, the optimal dropout rate was found to be 0.1, yielding a Precision of 79.30%, Recall of 76.86%, and F-measure of 77.72%. The highest accuracy for Mozilla was 79.54%, which reflected a 1.82% improvement over the average accuracy. These findings highlight the importance of fine-tuning dropout rates to achieve a balance between model generalization and predictive performance.  
Figure 8: Comparison of performance based on dropout parameter for Mozilla.  
Figure 7: Comparison of performance based on dropout parameter for Eclipse.  
Figure 9: Performance of the proposed model based on layer parameters.  
The study also investigated the impact of priority-based feature selection on the model's predictive capabilities. Figure 10 compares the performance of the proposed model with and without the application of feature selection.  
Across both datasets, the proposed model incorporating priority-based feature selection consistently outperformed the model without it. This result underscores the significance of extracting and utilizing priority-specific features in enhancing the model's ability to capture relevant patterns in the data.  
We note that the proposed Priority-Sensitive LSTM-Attention mechanism shows a robust performance in predicting bug report priorities. By optimizing hyperparameters, such as the number of layers and dropout rates, and integrating priority-based feature selection, the model achieves a high predictive accuracy and effectively generalizes across diverse datasets.