# A Comprehensive MCDM-Based Approach for Object-Oriented Metrics Selection Problems
## 5 Validation
### Fuzzy DEMATEL for Object-Oriented Metrics Results Discussion and Validation
\begin{table}
\begin{tabular}{|c|c c c c c|} \hline \hline \multirow{2}{*}{**Project**} & \multirow{2}{*}{**Defect**} & \multicolumn{3}{c|}{**Rule Selection Based on N**} & \multicolumn{3}{c|}{**Rule Selection based on Metrics Importance**} \\ \cline{3-6}  & & **Recall \%** & **Precision \%** & **F1 Score** & **Recall \%** & **Precision \%** & **F1 Score** \\ \hline \multirow{4}{*}{**Xerces v2.7**} & **Blob** & 55 & 64 & 59.16 & 66 & 88 & 75.43 \\ \cline{2-6}  & **LC** & 47 & 57 & 51.52 & 49 & 90 & 63.45 \\ \cline{2-6}  & **DC** & 52 & 68 & 58.93 & 71 & 70 & 70.50 \\ \cline{2-6}  & **FE** & 51 & 59 & 54.71 & 53 & 59 & 55.84 \\ \hline \multirow{4}{*}{**ArgoUML**} & **Blob** & 60 & 56 & 57.93 & 71 & 88 & 78.59 \\ \cline{2-6}  & **LC** & 51 & 65 & 57.16 & 53 & 65 & 58.39 \\ \cline{2-6}  & **DC** & 73 & 53 & 61.41 & 84 & 77 & 80.35 \\ \cline{2-6}  & **FE** & 38 & 62 & 47.12 & 62 & 67 & 64.40 \\ \hline \multirow{4}{*}{**Lucene 1.4**} & **Blob** & 100 & 80 & 88.89 & 100 & 86 & 92.47 \\ \cline{2-6}  & **LC** & 100 & 55 & 70.97 & 100 & 93 & 96.37 \\ \cline{2-6}  & **DC** & 100 & 71 & 83.04 & 100 & 91 & 95.29 \\ \cline{2-6}  & **FE** & 100 & 62 & 76.54 & 100 & 96 & 97.96 \\ \hline \multirow{4}{*}{**Log4j 1.2.1**} & **Blob** & 100 & 47 & 63.95 & 100 & 78 & 87.64 \\ \cline{2-6}  & **LC** & 100 & 49 & 65.77 & 100 & 89 & 94.18 \\ \cline{2-6}  & **DC** & 100 & 52 & 68.42 & 100 & 79 & 88.27 \\ \cline{2-6}  & **FE** & 100 & 56 & 71.79 & 100 & 94 & 96.91 \\ \hline \multirow{4}{*}{**GantProject**} & **Blob** & 54 & 69 & 60.59 & 75 & 77 & 75.99 \\ \cline{2-6}  & **LC** & 75 & 44 & 55.46 & 75 & 63 & 68.48 \\ \cline{1-1} \cline{2-6}  & **DC** & 90 & 49 & 63.45 & 90 & 82 & 85.81 \\ \cline{1-1} \cline{2-6}  & **FE** & 58 & 54 & 55.93 & 62 & 76 & 68.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The generated rules. \begin{table}
\begin{tabular}{|c|c c c c c c|} \hline \hline \multirow{2}{*}{**Project**} & \multirow{2}{*}{**Defect**} & \multicolumn{3}{c|}{**Rule Selection Based on N**} & \multicolumn{3}{c|}{**Rule Selection based on Metrics Importance**} \\ \cline{3-6}  & & **Recall \%** & **Precision \%** & **F1 Score** & **Recall \%** & **Precision \%** & **F1 Score** \\ \hline \multirow{4}{*}{**Xerces v2.7**} & **Blob** & 55 & 64 & 59.16 & 66 & 88 & 75.43 \\ \cline{2-6}  & **LC** & 47 & 57 & 51.52 & 49 & 90 & 63.45 \\ \cline{2-6}  & **DC** & 52 & 68 & 58.93 & 71 & 70 & 70.50 \\ \cline{2-6}  & **FE** & 51 & 59 & 54.71 & 53 & 59 & 55.84 \\ \hline \multirow{4}{*}{**ArgoUML**} & **Blob** & 60 & 56 & 57.93 & 71 & 88 & 78.59 \\ \cline{2-6}  & **LC** & 51 & 65 & 57.16 & 53 & 65 & 58.39 \\ \cline{2-6}  & **DC** & 73 & 53 & 61.41 & 84 & 77 & 80.35 \\ \cline{2-6}  & **FE** & 38 & 62 & 47.12 & 62 & 67 & 64.40 \\ \hline \multirow{4}{*}{**Lucene 1.4**} & **Blob** & 100 & 80 & 88.89 & 100 & 86 & 92.47 \\ \cline{2-6}  & **LC** & 100 & 55 & 70.97 & 100 & 93 & 96.37 \\ \cline{2-6}  & **DC** & 100 & 71 & 83.04 & 100 & 91 & 95.29 \\ \cline{2-6}  & **FE** & 100 & 62 & 76.54 & 100 & 96 & 97.96 \\ \hline \multirow{4}{*}{**Log4j 1.2.1**} & **Blob** & 100 & 47 & 63.95 & 100 & 78 & 87.64 \\ \cline{2-6}  & **LC** & 100 & 49 & 65.77 & 100 & 89 & 94.18 \\ \cline{1-1} \cline{2-6}  & **DC** & 100 & 52 & 68.42 & 100 & 79 & 88.27 \\ \cline{1-1} \cline{2-6}  & **FE** & 100 & 56 & 71.79 & 100 & 94 & 96.91 \\ \hline \multirow{4}{*}{**GantProject**} & **Blob** & 54 & 69 & 60.59 & 75 & 77 & 75.99 \\ \cline{2-6}  & **LC** & 75 & 44 & 55.46 & 75 & 63 & 68.48 \\ \cline{1-1} \cline{2-6}  & **DC** & 90 & 49 & 63.45 & 90 & 82 & 85.81 \\ \cline{1-1} \cline{2-6}  & **FE** & 58 & 54 & 55.93 & 62 & 76 & 68.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Detection results.