# Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory-Attention Mechanism
## 4 Experimental Result
### Results
#### 4.5.2 Comparison Results
* For Eclipse (H1\({}_{0}\), H2\({}_{0}\), H3\({}_{0}\), H4\({}_{0}\), H5\({}_{0}\), H6\({}_{0}\), H7\({}_{0}\)), there is no significant difference between the proposed model and the baseline models (Naive Bayes, Decision Tree, Random Forest, SVM, CNN, LSTM, and CNN-LSTM). * For Mozilla (H8\({}_{0}\), H9\({}_{0}\), H10\({}_{0}\), H11\({}_{0}\), H12\({}_{0}\), H13\({}_{0}\), H14\({}_{0}\)), there is no significant difference between the proposed model and the same baseline models. The alternative hypotheses to the null hypothesis are as follows. * H1a, H2a, H3a, H4a, H5a, H6a, and H7a: The proposed model and Naive Bayes, Decision Tree, Random Forest, SVM, CNN, LSTM, and CNN-LSTM differed for Eclipse. * H8a, H9a, H10a, H11a, H12a, H13a, and H14a: The proposed model and Naive Bayes, Decision Tree, Random Forest, SVM, CNN, LSTM, and CNN-LSTM differed for Mozilla. The evaluation utilized the F-measure metric to compute the normal distribution [18]. Depending on the results, either a \(t\)-test or a Wilcoxon test was performed to determine statistical significance. If the \(p\)-value was greater than or equal to 0.05, the \(t\)-test was applied. For \(p\)-values less than 0.05, the Wilcoxon test was used [9; 10]. The results of these tests are summarized in Table 1.  
For H1\({}_{0}\), the null hypothesis posited no significant difference between the proposed model and Naive Bayes for Eclipse. However, the \(p\)-value of 0.001953 obtained from the Wilcoxon test is less than 0.05, leading to the rejection of the null hypothesis and the acceptance of the alternative hypothesis. This indicates a statistically significant difference in performance between the proposed model and Naive Bayes. Similarly, for other hypotheses, the statistical verification results showed significant differences between the proposed model and all baseline methods across both datasets. The \(t\)-tests and Wilcoxon tests consistently produced \(p\)-values well below the 0.05 thresh \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hypothesis** & \(p\)**-Value** & **Result** \\ \hline H1\({}_{0}\) & (Wilcox test) 0.001953 & H1a: Accept \\ \hline H2\({}_{0}\) & (\(t\)-test) 4.222 \(\times\) 10\({}^{-16}\) & H2a: Accept \\ \hline H3\({}_{0}\) & (\(t\)-test) 3.619 \(\times\) 10\({}^{-12}\) & H3a: Accept \\ \hline H4\({}_{0}\) & (Wilcox test) 0.001953 & H4a: Accept \\ \hline H5\({}_{0}\) & (\(t\)-test) 1.657 \(\times\) 10\({}^{-12}\) & H5a: Accept \\ \hline H6\({}_{0}\) & (\(t\)-test) 1.579 \(\times\) 10\({}^{-12}\) & H6a: Accept \\ \hline H7\({}_{0}\) & (\(t\)-test) 4.366 \(\times\) 10\({}^{-13}\) & H7a: Accept \\ \hline H8\({}_{0}\) & (\(t\)-test) 1.411 \(\times\) 10\({}^{-8}\) & H8a: Accept \\ \hline H9\({}_{0}\) & (\(t\)-test) 7.015 \(\times\) 10\({}^{-14}\) & H9a: Accept \\ \hline H10\({}_{0}\) & (\(t\)-test) 1.015 \(\times\) 10\({}^{-11}\) & H10a: Accept \\ \hline H11\({}_{0}\) & (\(t\)-test) 3.826 \(\times\) 10\({}^{-10}\) & H11a: Accept \\ \hline H12\({}_{0}\) & (\(t\)-test) 4.66 \(\times\) 10\({}^{-10}\) & H12a: Accept \\ \hline H13\({}_{0}\) & (\(t\)-test) 4.497 \(\times\) 10\({}^{-12}\) & H13a: Accept \\ \hline H14\({}_{0}\) & (\(t\)-test) 2.158 \(\times\) 10\({}^{-10}\) & H14a: Accept \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical verification results. old, reinforcing the reliability of the observed improvements in the proposed model's performance. The statistical analysis conclusively shows that the proposed Priority-Sensitive LSTM-Attention mechanism outperforms traditional machine learning and deep learning baselines in predicting bug report priorities for both the Eclipse and Mozilla datasets. The results validate the robustness and effectiveness of the proposed model, with statistically significant differences confirmed across all comparisons.