# Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory-Attention Mechanism
## 4 Experimental Result
### Results
#### 4.5.2 Comparison Results  
We compared the proposed model to other models for bug report priority prediction in Figures 11 and 12 for Eclipse and Mozilla, respectively. Figure 11 shows a comparison between the proposed Priority-Sensitive LSTM-Attention model and various baseline models for predicting bug report priorities in the Eclipse dataset. The x-axis represents the accuracy percentages, while the y-axis lists the models. The proposed model achieved the highest accuracy of 84.11%, significantly outperforming all the baseline models. Figure 11: Comparison of LSTM–Attention and baseline models for Eclipse. Figure 10: Comparison of proposed model and non-feature-selection algorithm. Among the baseline models, CNN-LSTM showed the second-best performance with an accuracy of 72.57%, followed by LSTM at 56.59%. Traditional machine learning models such as Support Vector Machine (SVM), Decision Tree, Random Forest, and Naive Bayes exhibited similar performance levels, with accuracies ranging between 49.99% and 50.20%. The CNN model recorded the lowest performance among the deep learning approaches, with an accuracy of 41.36%. This comparison highlights the strength of the proposed model in handling the Eclipse dataset. Its superior performance can be attributed to the integration of priority-sensitive feature selection and the LSTM-Attention mechanism, which allows it to effectively capture sequential patterns and prioritize relevant features for bug report classification. Figure 12 presents the performance comparison for the Mozilla dataset, with the x-axis representing accuracy percentages and the y-axis listing the models. The proposed model achieved the highest accuracy of 93.00%, further solidifying its effectiveness across diverse datasets. Similar to the Eclipse dataset, CNN-LSTM was the second-best model with an accuracy of 71.65%, followed by LSTM at 51.84%. Traditional machine learning models such as SVM, Decision Tree, Random Forest, and Naive Bayes showed comparable performances, with accuracies clustered around 50.04% to 50.26%. The CNN model again recorded the lowest performance among the deep learning models, with an accuracy of 44.37%. The results for Mozilla underscore the robustness of the proposed LSTM-Attention model for handling large and complex datasets. Its ability to outperform both advanced deep learning models (e.g., CNN-LSTM) and traditional machine learning models shows the effectiveness of its priority-sensitive feature extraction and attention-based learning framework. The results in Figures 11 and 12 provide compelling evidence of the proposed model's superiority over baseline models across both the Eclipse and Mozilla datasets. The integration of LSTM and Attention mechanisms enables the proposed model to effectively capture sequential dependencies and focus on relevant features, resulting in significantly higher accuracy. Traditional machine learning models, while consistent in their performance, lag behind deep learning-based approaches due to their inability to leverage temporal and contextual information effectively. Among deep learning baselines, CNN-LSTM shows a competitive performance but falls short of the proposed model due to the absence of a priority-sensitive feature extraction mechanism. In Figures 13-16, the Precision, Recall, and F-measure are compared along with accuracy. Figure 12: Comparison of LSTM–Attention and baseline models for Mozilla. Figure 16: Comparison of baseline models’ (DL) performance with proposed model for Mozilla. Figure 14: Comparison of baseline models’ (DL) performance with proposed model for Eclipse. Figure 13: Comparison of baseline models’ (ML) performances with proposed model for Eclipse. Figure 15: Comparison of baseline models’ (ML) performance with proposed model for Mozilla. Figure 13 shows a performance comparison between traditional machine learning (ML) baseline models and the proposed Priority-Sensitive LSTM-Attention mechanism for the Eclipse dataset.