# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 5 Experimental Results
### Result
#### 5.5.2 Comparison Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hypothesis** & \(\boldsymbol{p}\)**-Value** & **Result** \\ \hline H1\({}_{0}\) & (_t_ test) 8.77 \(\times\) 10\({}^{-10}\) & H1\({}_{\text{a}}\): Accept \\ \hline H2\({}_{0}\) & (_t_ test) 7.47 \(\times\) 10\({}^{8}\) & H2\({}_{\text{a}}\): Accept \\ \hline H3\({}_{0}\) & (_t_ test) 3.66 \(\times\) 10\({}^{-11}\) & H3\({}_{\text{a}}\): Accept \\ \hline H4\({}_{0}\) & (_t_ test) 2.19 \(\times\) 10\({}^{-14}\) & H4\({}_{\text{a}}\): Accept \\ \hline H5\({}_{0}\) & (_t_ test) 1.43 \(\times\) 10\({}^{-13}\) & H5\({}_{\text{a}}\): Accept \\ \hline H6\({}_{0}\) & (_t_ test) 1.48 \(\times\) 10\({}^{-8}\) & H6\({}_{\text{a}}\): Accept \\ \hline H7\({}_{0}\) & (_t_ test) 6.31 \(\times\) 10\({}^{-9}\) & H7\({}_{\text{a}}\): Accept \\ \hline H8\({}_{0}\) & (_t_ test) 9.66 \(\times\) 10\({}^{-9}\) & H8\({}_{\text{a}}\): Accept \\ \hline H9\({}_{0}\) & (_t_ test) 2.20 \(\times\) 10\({}^{-16}\) & H9\({}_{\text{a}}\): Accept \\ \hline H10\({}_{0}\) & (_t_ test) 2.20 \(\times\) 10\({}^{-16}\) & H10\({}_{\text{a}}\): Accept \\ \hline H11\({}_{0}\) & (_t_ test) 1.40 \(\times\) 10\({}^{-8}\) & H11\({}_{\text{a}}\): Accept \\ \hline H12\({}_{0}\) & (_t_ test) 1.53 \(\times\) 10\({}^{-8}\) & H12\({}_{\text{a}}\): Accept \\ \hline H13\({}_{0}\) & (_t_ test) 1.74 \(\times\) 10\({}^{-9}\) & H13\({}_{\text{a}}\): Accept \\ \hline H14\({}_{0}\) & (_t_ test) 2.20 \(\times\) 10\({}^{-16}\) & H14\({}_{\text{a}}\): Accept \\ \hline H15\({}_{0}\) & (_t_ test) 2.20 \(\times\) 10\({}^{-16}\) & H15\({}_{\text{a}}\): Accept \\ \hline H16\({}_{0}\) & (_t_ test) 1.70 \(\times\) 10\({}^{-10}\) & H16\({}_{\text{a}}\): Accept \\ \hline H17\({}_{0}\) & (_t_ test) 1.37 \(\times\) 10\({}^{-10}\) & H17\({}_{\text{a}}\): Accept \\ \hline H18\({}_{0}\) & (_t_ test) 5.94 \(\times\) 10\({}^{-12}\) & H18\({}_{\text{a}}\): Accept \\ \hline H19\({}_{0}\) & (_t_ test) 2.20 \(\times\) 10\({}^{-16}\) & H19\({}_{\text{a}}\): Accept \\ \hline H20\({}_{0}\) & (Wilcox-test) 5.89 \(\times\) 10\({}^{-3}\) & H20\({}_{\text{a}}\): Accept \\ \hline H21\({}_{0}\) & (Wilcox-test) 1.95 \(\times\) 10\({}^{-3}\) & H21\({}_{\text{a}}\): Accept \\ \hline H22\({}_{0}\) & (_t_ test) 1.74 \(\times\) 10\({}^{-10}\) & H22\({}_{\text{a}}\): Accept \\ \hline H23\({}_{0}\) & (_t_ test) 4.58 \(\times\) 10\({}^{-11}\) & H23\({}_{\text{a}}\): Accept \\ \hline H24\({}_{0}\) & (_t_ test) 2.20 \(\times\) 10\({}^{-16}\) & H24\({}_{\text{a}}\): Accept \\ \hline H25\({}_{0}\) & (_t_ test) 2.20 \(\times\) 10\({}^{-16}\) & H25\({}_{\text{a}}\): Accept \\ \hline H26\({}_{0}\) & (_t_ test) 1.09 \(\times\) 10\({}^{-11}\) & H26\({}_{\text{a}}\): Accept \\ \hline H27\({}_{0}\) & (_t_ test) 2.04 \(\times\) 10\({}^{-8}\) & H27\({}_{\text{a}}\): Accept \\ \hline H28\({}_{0}\) & (_t_ test) 9.48 \(\times\) 10\({}^{-11}\) & H28\({}_{\text{a}}\): Accept \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical verification results. The proposed model significantly outperformed the baseline models in all evaluated metrics, including accuracy, precision, recall, and F1-measure. The statistical tests further validated these findings, confirming the robustness and reliability of the proposed model for detecting duplicate bug reports across a variety of software projects. These results underscore the effectiveness of the proposed approach in addressing the challenges of duplicate bug report detection.