# A Novel UCP Model Based on Artificial Neural Networks and Orthogonal Arrays
## 3 New, Improved UCP--Our Approach
### The Methodology Used within the Improved UCP Model
#### Step 3:
The slightest influence has the input value of UUCW (Dataset_4), and the change in the value of MMRE is, in this case, lower by 0.5%, which would mean that the error can be reduced/increased if the observed values are further analyzed. It can be concluded that the architecture with six input sizes can be replaced with the architecture with four input sizes. That is, in the observed approach, the ANN-L16 architecture can be replaced with the ANN-L36 prim architecture (Table 10). From Table 11, it can be concluded that the dependent variable UUCP has less impact than the dependent variable AUCP. The most significant influence of AUCP (Dataset_3) on the change in the MMRE value is 0.5%. The slightest influence of AUCP (Dataset_1) on the change in the MMRE value is \(-\)0.3%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **MMRE** & **UAW** & **UUCW** & **UUCP** & **TCF** & **ECF** & **AUCP** \\ \hline Dataset\_1 & 6.7\% & 7.1\% & 6.7\% & 7.0\% & 6.7\% & 6.7\% & 7.1\% \\ Dataset\_2 & 7.0\% & 7.1\% & 7.0\% & 7.2\% & 6.9\% & 7.1\% & 7.2\% \\ Dataset\_3 & 8.0\% & 7.9\% & 8.1\% & 7.9\% & 8.1\% & 8.1\% & 7.5\% \\ Dataset\_4 & 8.3\% & 7.9\% & 8.4\% & 7.9\% & 8.3\% & 8.2\% & 8.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Influence of the input values on the change in MMRE (UCP). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Training** \\ \hline PRED (\%) & ANN-L16 (\%) & ANN-L36 prim (\%) \\ PRED(25) & 100.0 & 100.0 \\ PRED(30) & 100.0 & 100.0 \\ PRED(50) & 100.0 & 100.0 \\ \hline \hline \multicolumn{3}{c}{**Testing**} \\ \hline PRED(25) & 100.0 & 100.0 \\ PRED(30) & 100.0 & 100.0 \\ PRED(50) & 100.0 & 100.0 \\ \hline \multicolumn{3}{c}{**Validation1**} \\ \hline PRED(25) & 100.0 & 100.0 \\ PRED(30) & 100.0 & 100.0 \\ PRED(50) & 100.0 & 100.0 \\ \hline \multicolumn{3}{c}{**Validation2**} \\ \hline PRED(25) & 100.0 & 100.0 \\ PRED(30) & 100.0 & 100.0 \\ PRED(50) & 100.0 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Prediction values (UCP). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **UUCP** & **g – UUCP** = MMRE – UUCP** & **AUCP** & **g – AUCP** = MMRE – AUCP** \\ \hline Dataset\_1 & 6.9\% & \(-\)0.1\% & 6.8\% & \(-\)0.3\% \\ Dataset\_2 & 7.1\% & \(-\)0.1\% & 7.1\% & \(-\)0.1\% \\ Dataset\_3 & 8.0\% & 0.1\% & 8.0\% & 0.5\% \\ Dataset\_4 & 8.2\% & 0.2\% & 8.2\% & 0.2\% \\ \hline \hline  & max & 0.2\% & max & 0.5\% \\  & min & \(-\)0.1\% & min & \(-\)0.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Influence of dependent variables (UUCP and AUCP) on the change in the MMRE value. A graphical representation of the dependent input values of UUCP and AUCP with the values of their errors is given in Figure 9.  
By comparing the results of the parametric method COCOMO2000 with improved COCOMO2000 and ANN, it can be concluded that the model error is reduced by 193.1/43.3 = 4.5 times. In the second proposed approach, comparing the parametric method COCOMO2000 and the improved COSMIC FP and ANN, the model error reduction is 193.1/28.8 = 6.7 times. Compared with the COCOMO2000 parametric method with UCP and ANN, the model error reduction is 193.1/7.5 = 25.7 times (Table 12; Figure 10). In the first proposed approach, the lowest model error value is 43.3% for the ANN-L36 architecture. In the second proposed approach, the lowest error value is achieved with ANN-L36 prim, with a value of 28.8%. In the third proposed approach, both proposed architectures, ANN-L16 and ANN-L36 prim, give the lowest model error value of 7.5% (Table 12; Figure 10). It can be concluded that the third proposed UCP approach achieves the lowest MMRE value. In addition, the ANN-L16 architecture in this approach converges rapidly and reaches the \"stop criterion\" after the fourth iteration, which is also the lowest number of repeated iterations that apply to all architectures used in all three proposed approaches. The influence of dependent variables on the change of MMRE values in the ANN-L16 architecture is less than 0.5%.