## 5 Results and Evaluation
### Comparative Analysis
#### 5.2.2 Performance Comparison with DupPR-Complementary Data  
By using common benchmark DupPR-complementary data, we validate the proposed GDN-GS approach in this case. The comparing of our proposed as well as existing state-of-the-art methodologies for DupPR-complementary data is shown in Table 6. The accuracy our proposed GDN-GS technique is more accurate and efficient than the existing techniques, PRs-text, PRs-text-similarity, PRs-improved, support vector machine, k-nearest neighbour, random forest, hierarchical clustering, and K-means clustering, by 9.76%, 15.16%, 20.56%, 25.96%, 31.36%, 36.76%, and 47.55%, respectively. The precision our proposed GDN \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Repository Names**} & \multirow{2}{*}{**Full-Requests**} & \multicolumn{3}{c}{**Performance Metrics (\%)**} & \multicolumn{1}{c}{} \\ \cline{3-8}  & & **Accuracy** & **Precision** & **Recall** & **F-Measure** & **SRE** \\ \hline angular/angularjs & 31 & 92.530 & 92.107 & 93.145 & 92.621 & 91.230 \\ facebook/react & 15 & 92.542 & 92.119 & 93.152 & 92.633 & 91.242 \\ twbs/boostrap & 47 & 92.554 & 92.131 & 93.164 & 92.645 & 91.254 \\ symfony/symfony & 33 & 92.566 & 92.143 & 93.176 & 92.657 & 91.266 \\ rails/rails & 25 & 92.578 & 92.155 & 93.188 & 92.669 & 91.278 \\ joomla/joomla-cms & 19 & 92.590 & 92.167 & 93.200 & 92.681 & 91.290 \\ ansible/ansible & 18 & 92.602 & 92.179 & 93.212 & 92.693 & 91.302 \\ nodejs/node & 15 & 92.614 & 92.191 & 93.224 & 92.705 & 91.314 \\ cocos2d/cocos2d-x & 3 & 92.626 & 92.203 & 93.236 & 92.717 & 91.326 \\ rust-lang/rust & 9 & 92.638 & 92.215 & 93.248 & 92.729 & 91.338 \\ ccph/ceph & 9 & 92.650 & 92.227 & 93.264 & 92.741 & 91.350 \\ zendframework/zf2 & 9 & 92.662 & 92.239 & 93.272 & 92.753 & 91.362 \\ django/django & 3 & 92.674 & 92.251 & 93.284 & 92.765 & 91.374 \\ pydata/pandas & 3 & 92.686 & 92.263 & 93.296 & 92.777 & 91.386 \\ elastic/elasticsearch & 6 & 92.698 & 92.275 & 93.308 & 92.789 & 91.398 \\ JuliaLang/julia & 3 & 92.710 & 92.287 & 93.324 & 92.801 & 91.410 \\ scikit-learn/scikit-learn & 3 & 92.722 & 92.299 & 93.332 & 92.813 & 91.422 \\ kubernetes/kubernetes & 13 & 92.734 & 92.311 & 93.344 & 92.825 & 91.434 \\ docker/docker & 7 & 92.746 & 92.323 & 93.356 & 92.837 & 91.446 \\ symfony/symfony-docs & 19 & 92.758 & 92.335 & 93.368 & 92.849 & 91.458 \\ \hline Average & & 92.644 & 92.221 & 93.254 & 92.734 & 91.344 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation results of our proposed GDN-GS technique. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Detection Techniques**} & \multicolumn{3}{c}{**Metrics (\%)**} & \multicolumn{1}{c}{} \\ \cline{2-5}  & **Accuracy** & **Precision** & **Recall** & **F-Measure** & **SRE** \\ \hline GDN-GS & 92.644 & 92.221 & 93.254 & 92.735 & 91.344 \\ PRs-text [21] & 87.414 & 86.991 & 88.024 & 87.505 & 86.114 \\ PRs-text-similarity [32] & 82.184 & 81.761 & 82.794 & 82.275 & 80.884 \\ PRs-Improved [33] & 76.954 & 76.531 & 77.564 & 77.045 & 75.654 \\ k-nearest neighbor & 66.494 & 66.071 & 67.104 & 66.585 & 65.194 \\ Random forest & 61.264 & 60.841 & 61.874 & 61.355 & 59.964 \\ Support vector machine & 71.724 & 71.301 & 72.334 & 71.815 & 70.424 \\ Hierarchical clustering & 56.034 & 55.611 & 56.644 & 56.125 & 54.734 \\ K-means clustering & 50.804 & 50.381 & 51.414 & 50.895 & 49.504 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis with DupPR-basic dataset. GS technique is more precise and efficient than existing techniques PRs-text, PRs-text-similarity, PRs-improved, support vector machine, k-nearest neighbour, random forest, hierarchical clustering, and K-means clustering by 8.65 percent, 14.14%, 19.64%, 25.13%, 30.62%, 36.11%, and 47.10%, respectively.