## 5 Results and Evaluation
### Comparative Analysis  
#### 5.2.1 Performance Comparison with DupPR-Basic Dataset  
In this scenario, we validate the proposed GDN-GS technique by using the standard benchmark DupPR-basic dataset. Table 4 describes the simulation results of our proposed GDN-GS technique with the GitHub repositories used in our evaluation process. The evaluation approach took into account 20 repositories, as shown in the table, and validated model by using various performance measures, including saved reviewing effort (SRE), F-measure, recall, precision, and accuracy. Table 5 describes the comparative analysis of our proposed and existing state-of-art techniques for the DupPR-basic dataset.  
The accuracy of our proposed GDN-GS technique outperforms the existing PRs-text, PRs-text-similarity, PRs-improved, support vector machine, k-nearest neighbor, random forest, hierarchical clustering, and K-means clustering techniques by 5.65%, 11.29%, 16.9%, 22.58%, 28.26%, 33.87%, and 45.16%, respectively. The precision our proposed GDN-GS technique is more precise than existing techniques PRs-text, PRs-text-similarity, PRs-improved, support vector machine, k-nearest neighbour, random forest, hierarchical clustering, and K-means clustering, respectively, by 5.67%, 11.34%, 17.13%, 22.68%, 28.36%, 34.27%, 39.69%, and 45.39%, respectively. The recall of our proposed GDN-GS technique is 5.61%, 11.22%, 16.83%, 22.43%, 28.04%, 33.65%, 39.26%, and 44.87% more efficient, respectively, than the existing techniques in terms of PRs-text, PRs-text-similarity, PRs-improved, support vector machine, k-nearest neighbour, random forest, hierarchical clustering, and K-means clustering, respectively. The F-measure of our proposed GDN-GS technique is 5.64%,  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multirow{2}{*}{**Attributes**} & \multicolumn{2}{c}{**Number of Pull-Requests**} \\ \cline{2-3}  & **Overall** & **Duplicate** \\ \hline Number of pull-requests & 333,200 & 3619 \\ Number of contributors & 39,776 & 2589 \\ Number of reviewers & 24,071 & 2830 \\ Number of review comments & 2,191,836 & 39,945 \\ Number of checks & 364,646 & 4413 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset description of DupPR-basic data.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multirow{2}{*}{**Attributes**} & \multicolumn{2}{c}{**Number of Pull-Requests**} \\ \cline{2-3}  & **Overall** & **Duplicate** \\ \hline Number of pull-requests & 1,144,251 & 3978 \\ Number of contributors & 29,978,291 & 2789 \\ Number of reviewers & 126,697 & 3145 \\ Number of review comments & 2,228,894 & 42,515 \\ Number of issues comments & 2,886,006 & 4878 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset description of DupPR-complementary data.  
11.28%, 16.92%, 22.56%, 28.20%, 33.84%, 39.48%, and 45.12% more efficient, respectively, than existing techniques, which include support vector machines, k-nearest neighbours, random forests, hierarchical clustering, and K-means clustering. SRE of proposed GDN-GS technique outperforms the state-of-the-art PRs-text, PRs-text-similarity, PRs-improved, support vector machine, k-nearest neighbour, random forest, hierarchical clustering, and K-means clustering techniques by 5.73%, 11.45%, 17.18%, 22.90%, 28.63%, 34.35%, and 45.80%, respectively.