# Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory-Attention Mechanism
## 4 Experimental Result
### Results
#### 4.5.2 Comparison Results
The evaluation metrics include Precision, Recall, and F-measure, which are plotted along the x-axis for the different models (Naive Bayes, Random Forest, Decision Tree, SVM, and the proposed model), while the y-axis represents the corresponding performance values. The proposed model significantly outperformed all the baseline models across all three metrics. The Precision of the proposed model reached 92.17%, compared to the highest baseline Precision of 49.12% achieved by Random Forest. Similarly, the Recall and F-measure of the proposed model were also substantially higher, reflecting its ability to correctly identify relevant bug priorities with fewer false positives and false negatives. In contrast, the baseline models showed relatively similar and lower performance, with SVM achieving a Recall of 39.84% and Decision Tree yielding an F-measure of 39.54%. This stark difference highlights the limitations of traditional ML approaches in handling the complex relationships and temporal dependencies within bug report datasets. The proposed model's superior performance can be attributed to its integration of LSTM and Attention mechanisms, which effectively capture sequential patterns and prioritize relevant features in the data. Figure 14 illustrates a comparison between deep learning models, including CNN, LSTM, CNN-LSTM, and the proposed Priority-Sensitive LSTM-Attention mechanism on the Eclipse dataset. The proposed model achieved the highest performance, with a Precision of 92.17%, Recall of 92.17%, and F-measure of 92.17%. This clearly outperformed all baseline models. Among the baselines, CNN showed the lowest performance, with a Precision of 15.03%, Recall of 15.03%, and F-measure of 15.03%, indicating its inability to effectively process sequential dependencies. LSTM improved upon this, achieving a Precision of 34.05%, but it still struggled to capture complex patterns in the data. CNN-LSTM, which combines feature extraction and sequential modeling, performed better than the standalone CNN and LSTM models, reaching a Precision of 64.73%, yet it fell significantly short of the proposed model. The results confirm the effectiveness of the proposed model in leveraging priority-sensitive features and attention mechanisms to deliver superior predictive performance. Figure 15 compares the performance of machine learning models, including Naive Bayes, Random Forest, Decision Tree, and SVM, with the proposed model on the Mozilla dataset. The proposed model showed clear superiority, achieving a Precision of 79.54%, Recall of 79.54%, and F-measure of 79.54%. The traditional machine learning models, such as Naive Bayes and Random Forest, exhibited limited performances, with Precision values of 44.42% and 49.44%, respectively. Decision Tree and SVM showed even lower Recall and F-measure values, with Decision Tree reaching only 34.86% in Recall. The proposed model's significantly higher scores highlight its ability to handle large-scale and complex data with enhanced Accuracy, Precision, and Recall, reinforcing its robustness in bug priority prediction tasks. Figure 16 presents the performance comparison for the Mozilla dataset between CNN, LSTM, CNN-LSTM, and the proposed model. Similar to the results for Eclipse, the proposed model achieved the highest Precision, Recall, and F-measure values, all at 79.54%. CNN, as the weakest baseline model, showed a Precision of 22.73%, Recall of 22.73%, and F-measure of 22.73%, reflecting its limitations in handling the complexity of bug report data. LSTM improved upon these values, reaching a Precision of 30.58%. CNN-LSTM showed a better performance, with a Precision of 64.72%, but it still fell short compared to the proposed model. The results indicate that the proposed model's combination of LSTM and Attention mechanisms, along with priority-sensitive feature selection, provides a significant advantage over traditional deep learning approaches. The null hypothesis for statistical verification is as follows.