## 5 Experimental Results
### RQ2. How Effective Is the Proposed Approach in Test Case Prioritization in Testing CAD Software?
_Precision_ is the proportion of returned results that are truly correct; _Recall_ is the proportion of the truly correct results in all retrieved results from the test set; _F-score_ is the trade-off between _Precision_ and _Recall_; _Accuracy_ is the proportion of correct results in all results from the whole data set; _Time_ is the time consumption on the test set; and _Memory_ is the memory consumption on the test set. N/A indicates the denominator is zero. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Data Set** & **Test Set** & **Ranking Model** & **Top-10** & **Top-20** & **Top-30** & **APFD** \\ \hline \multirow{8}{*}{Datasets 1} & & CART & 8 & 8 & 8 & 59.09\% \\  & & RF & 8 & 8 & 8 & **85.23\%** \\  & & SVM & 8 & 8 & 8 & 61.36\% \\  & & BN & 5 & 8 & 8 & 71.59\% \\  & & CNN & 8 & 8 & 8 & 61.36\% \\  & & RNN & 8 & 8 & 8 & 61.36\% \\  & & RR & 7 & 8 & 8 & 48.86\% \\ \cline{2-6}  & & CART & 14 & 19 & 27 & 65.29\% \\  & & RF & 17 & 29 & 33 & **72.83\%** \\  & & SVM & 16 & 23 & 27 & 69.10\% \\  & & BN & 13 & 21 & 30 & 65.57\% \\  & & CNN & 16 & 23 & 27 & 69.10\% \\  & & RNN & 16 & 23 & 27 & 69.10\% \\  & & RR & 15 & 16 & 16 & 53.66\% \\ \hline \multirow{8}{*}{procedural} & & CART & 24 & 24 & 24 & 70.49\% \\  & & RF & 24 & 24 & 24 & 80.21\% \\  & & SVM & 24 & 24 & 24 & 84.38\% \\  & & BN & 7 & 24 & 24 & **90.63\%** \\  & & CNN & 24 & 24 & 24 & 84.38\% \\  & & RNN & 24 & 24 & 24 & 84.38\% \\  & & RR & 23 & 24 & 24 & 48.96\% \\ \hline \multirow{8}{*}{Fold 1} & & CART & 8 & 8 & 10 & 66.5\% \\  & & RF & 9 & 10 & 10 & **73.03\%** \\  & & SVM & 8 & 9 & 10 & 63.66\% \\  & & BN & 8 & 8 & 9 & 67.54\% \\  & & CNN & 8 & 9 & 10 & 63.66\% \\  & & RNN & 8 & 9 & 10 & 63.66\% \\  & & RR & 7 & 8 & 8 & 58.11\% \\ \cline{2-6}  & & CART & 5 & 5 & 5 & 72.9\% \\  & & RF & 5 & 5 & 5 & 73.38\% \\  & & SVM & 5 & 5 & 5 & **73.5\%** \\  & & BN & 3 & 5 & 5 & 62.07\% \\  & & CNN & 5 & 5 & 5 & 73.5\% \\  & & RNN & 5 & 5 & 5 & 73.5\% \\  & & RR & 4 & 5 & 5 & 69.72\% \\ \hline \multirow{8}{*}{Fold 3} & & CART & 6 & 9 & 12 & 66.14\% \\  & & RF & 8 & 12 & 13 & **73.51\%** \\  & & SVM & 6 & 10 & 13 & 67.12\% \\ \cline{1-1}  & & BN & 7 & 11 & 13 & 68.94\% \\ \cline{1-1}  & & CNN & 6 & 10 & 13 & 67.12\% \\ \cline{1-1}  & & RNN & 6 & 10 & 13 & 67.12\% \\ \cline{1-1}  & & RR & 5 & 8 & 11 & 58.39\% \\ \hline \end{tabular}
\end{table} 
Table 5: Evaluation results of the six models and the baseline of random rankings. We show the result in top-10, top-20, top-30, and the APFD. The top-\(k\) metric indicates the number of defects that are found by the top \(k\) test cases in an ordered sequence of test cases. The zero value in the evaluation metrics means that no defective method is detected by the test cases. **Conclusion**. Experimental results show that the random forest model is able to find the highest number of defects in the top-\(k\) metric compared with the other six models. This means that the random forest model is more capable of ranking test cases. Therefore, we consider that the random forest should be an effective choice for finding defects.