## 3 Software Cost Model Considering Operating Environment
### Results of Model Comparison  
Tables 5-7 summarize the parameters of the eight models in Table 1 estimated via the LSE technique and the values of the 12 common criteria (RMSE, PRR, PP, AIC, BIC, PRV, RMSPE, TS, PC, PIC, R\({}^{2}\), and Adj R\({}^{2}\)). We derived the 12 common criteria for t = 1, 2, \(\cdots\), 17 in Dataset #1 (Table 2), the exposure times (cumulative system days) in Dataset #2 (Table 3), and t = 1, 2, \(\cdots\), 18 in Dataset #3 (Table 4). Table 8 shows that the new model has the lowest RMSE, PRR, PP, AIC, BIC, PRV, RMSPE, TS, PC, and PIC values among all the models. The new model has the highest R\({}^{2}\) and Adj R\({}^{2}\) values among all the models, and its RMSE is 7.7531. The PRR, PP, AIC, BIC, PRV, RMSPE, TS, PC, and PIC values of the proposed model are 0.0407, 0.0390, 150.5078, 153.8407, 6.9884, 6.9885, 3.8163, 29.8046, and 786.3898, respectively, which are significantly smaller than those of the other models. The R\({}^{2}\) and Adj R\({}^{2}\) values of the proposed model are 0.9605 and 0.9473, respectively. Table 9 shows that the RMSE, PRV, RMSPE, TS, and PIC values for the proposed model are the lowest among those of the other models; its PRR and PP values are the second lowest. R\({}^{2}\) and Adj R\({}^{2}\) are the highest among those of all the models, and their RMSE is 0.8961. The PRV, RMSPE, TS, and PIC values of the proposed model are 0.7762, 0.7763, 0.7877, and 12.5607, respectively, which are significantly smaller than those of the other models. The R\({}^{2}\) and Adj R\({}^{2}\) values of the proposed model are 0.9935 and 0.9902, respectively. As shown in Table 10, the proposed model has the lowest RMSE, PRR, PP, AIC, BIC, PRV, RMSPE, TS, PC, and PIC values among all the models. Its R\({}^{2}\) and Adj R\({}^{2}\) values are the highest, and the RMSE value is 2.7432. The PRR, PP, AIC, BIC, PRV, RMSPE, TS, PC, and PIC values of the proposed model are 0.2297, 0.2243, 98.2713, 101.8328, 2.4891, 2.4894, 6.3347, 17.2255, and 110.2058, respectively, which are significantly smaller than those of the other models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Time Index** & **Failures** & **Cumulative Failures** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 4: CM dataset—Dataset #3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**System Days** & **Failures** & **Cumulative Failures** \\ \hline
32,299 & 2 & 26 \\
40,594 & 4 & 30 \\
49,476 & 1 & 31 \\
55,596 & 0 & 31 \\
58,061 & 1 & 32 \\
58,588 & 1 & 33 \\
58,633 & 0 & 33 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 3a-c show the MVF plots for all eight models for Datasets #1 to #3, respectively. The black dots represent the actual values, and the red lines represent the predicted values of the proposed model. Figure 4a-c show that the relative error of the SRM more rapidly approaches zero than those of the other models, confirming its ability to provide more accurate predictions. Tables A1-A3 list the 95% confidence intervals of all eight models for Datasets #1-#3, respectively. In addition, Figure 5a-c plot the MVF of the proposed model and its 95% confidence interval for Datasets #1 and #3. The solid line represents the predicted value of the proposed model, and the dotted lines represent the upper confidence limits (UCLs) and lower confidence limits (LCLs). Figure 4: Relative errors of eight models in Table 1.  
Figure 5: 95% confidence interval of the proposed model.