# Prediction and Comparative Analysis of Software Reliability Model Based on NHPP and Deep Learning
## 3 Numerical Example
### Results
#### 3.3.2 Results of Dataset 2
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**No.** & **Model** & **MSE** & **MAE** & **PRR** & **PP** & **R\({}^{2}\)** & **PRV** & **RMSPE** & **MEOP** & **TS** & **PC** & **preSSE** \\ \hline
1 & GO & 514.6996 & 20.0326 & 17.3582 & 100.887 & 0.8664 & 21.0794 & 22.7819 & 19.8400 & 23.7311 & 322.5348 & 735,386.2 \\
2 & DS & 13.7076 & 3.0740 & 225.700 & 8.8015 & 0.9964 & 3.6874 & 3.7202 & 3.0445 & 3.8728 & 135.8146 & 202,541.7 \\
3 & YID & 12.1406 & 2.8671 & 215.3384 & 7.1101 & 0.9968 & 3.4643 & 3.5010 & 2.8395 & 3.6447 & 129.5631 & 184,160.8 \\
4 & PZ & 10.0437 & 2.5378 & 329.7079 & 9.6331 & 0.9974 & 3.1828 & 3.1847 & 2.5134 & 3.3150 & 119.7981 & 174,920.2 \\
5 & PNZ & 9.6155 & 2.4483 & 7.2913 & 1.9345 & 0.9975 & 3.1155 & 3.1160 & 2.4248 & 3.2436 & 117.5593 & 16.6438 \\
6 & TP & 11.0679 & 2.6448 & 5.4184 & 1.7716 & 0.9971 & 3.3366 & 3.3430 & 2.6194 & 3.4800 & 124.7987 & 2,037,721 \\
7 & TC & 13.4688 & 3.0404 & 2453.543 & 9.2252 & 0.9965 & 3.6077 & 3.6872 & 3.0112 & 3.8389 & 134.9098 & 189,842.4 \\
8 & Vtub & 10.7640 & 2.7480 & 5303.130 & 7.7007 & 0.9972 & 3.2707 & 3.2966 & 2.7216 & 3.4318 & 123.3649 & 159,551.2 \\
9 & DPF & 16.6392 & 3.3789 & 5.4085 & 88.826 & 0.9957 & 4.0398 & 4.0985 & 3.4644 & 4.2668 & 145.7959 & 305,246.7 \\
10 & UDPF & 22.5801 & 3.8694 & 99.4808 & 9.7126 & 0.9941 & 4.6880 & 4.7742 & 3.8322 & 4.9705 & 161.5192 & 274,796.2 \\
11 & DNN & 5.8181 & 2.1792 & 1.5342 & 4.0310 & 0.9985 & 1.0391 & 2.4143 & 2.1583 & 2.5231 & 91.6808 & 355.63 \\
12 & RNN & 4.8223 & 2.0739 & 2.1101 & 9.9452 & 0.9987 & 0.7255 & 2.1971 & 2.0539 & 2.2970 & 82.0123 & 71.06 \\
13 & LSTM & 4.6712 & 2.0805 & 2.3802 & 16.1958 & 0.9988 & 0.5882 & 2.1621 & 2.0605 & 2.2608 & 80.3730 & 290.41 \\
14 & GRU & 4.5494 & 2.0688 & 2.1001 & 9.9841 & 0.9988 & 0.5218 & 2.1336 & 2.0489 & 2.2311 & 79.0128 & 347.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of all criteria using 80% of Dataset 2. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**No. ** & **Model** & **MSE** & **MAE** & **PRR** & **PP** & **R\({}^{2}\)** & **PRV** & **RMSPE** & **MEOP** & **TS** & **PC** & **preSSE** \\ \hline
1 & GO & 1278.733 & 30.0077 & 23.6240 & 173.2001 & 0.8256 & 33.3020 & 35.8928 & 29.7512 & 28.0264 & 415.9016 & 426,981.8 \\
2 & DS & 98.7794 & 7.0277 & 2204.006 & 8.9742 & 0.9865 & 9.8441 & 9.9807 & 6.9676 & 7.7895 & 267.3789 & 114,300.6 \\
3 & YID & 97.1781 & 6.9453 & 840.7242 & 8.4733 & 0.9867 & 9.7596 & 9.8995 & 6.8859 & 7.7261 & 266.4310 & 113,140.1 \\
4 & PZ & 27.9916 & 4.2381 & 677.6398 & 67.5837 & 0.9962 & 5.3037 & 5.3136 & 4.2019 & 4.1466 & 194.2418 & 26,776.2 \\
5 & PNZ & 39.4936 & 4.7960 & 18.9956 & 3.8443 & 0.9946 & 6.2657 & 6.3113 & 4.7550 & 4.9254 & 214.2075 & 1,165,855 \\
6 & TP & 13.6426 & 2.9286 & 4.0108 & 1.6448 & 0.9981 & 3.6944 & 3.7095 & 2.9035 & 2.8948 & 152.5569 & 1,390,597 \\
7 & TC & 64.3763 & 6.6342 & 19,2283 & 14.4663 & 0.9912 & 7.8678 & 8.0567 & 6.5775 & 6.2884 & 242.5466 & 65,012.80 \\
8 & Vtub & 26.1124 & 3.8598 & 3.7169 & 14.301 & 0.9964 & 5.1293 & 5.1322 & 3.8268 & 4.0050 & 190,2112 & 38,868.05 \\
9 & DPF & 50.3622 & 6.2521 & 9.2044 & 251.4521 & 0.9931 & 7.0064 & 7.1264 & 6.1987 & 5.5620 & 228.3074 & 28,590.91 \\
10 & UDPF & 36.7890 & 4.7296 & 4.2437 & 47.5652 & 0.9950 & 6.0896 & 6.0917 & 4.6891 & 4.7537 & 210.0929 & 33,114.75 \\
11 & DNN & 11.4930 & 2.845 before the black line are estimated values, and the points after the black line are predicted values.