# Software Product Line Maintenance Using Multi-Objective Optimization Techniques
## 6 Results and Discussion
### Results Generated by Quality Indicators
On the contrary, an unacceptable performance was demonstrated by IBEA and NSGAII, and the generated values were (0.36 and 0.0771) and (0.4677 and 0.0256), respectively. When the results of Coche Ecologico are investigated, it is found that approximately the same average values were generated by both the MOEA/D and SPEA2, with a slight change in standard deviation, (0.4868 and 0.0048) and (0.5025 and 0.0013). Moreover, NSGAIII yielded good result values (0.4541 and 0.0329) for the Smart Homev2.2 _FM_. Though IBEA and NSGAII illustrated poor performances, they generated (0.3257 and 0.0802) and (0.3227 and 0.0107) values, respectively. In Table 4, it is examined that, for all the feature models, the SPEA2 algorithm generated the best average and standard deviation values using the spacing metric. However, the MOEA/D and NSGAIII algorithms also generated good values for the average and standard deviation. To conclude, in Table 4, it is determined that SPEA2 and MOEA/D can produce better values. It is worth mentioning that NSGAIII is also good at producing a desirable result for most of the feature models. Nevertheless, unsatisfactory performances are revealed by IBEA and NSGAII as compared to the other three algorithms. \begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Indicator**} & \multirow{2}{*}{**Feature Models**} & \multicolumn{2}{c}{**IBEA**} & \multicolumn{2}{c}{**MOEAD**} & \multicolumn{2}{c}{**NSGAII**} & \multicolumn{2}{c}{**NSGAIII**} & \multicolumn{2}{c}{**SPEA2**} \\ \cline{3-11}  & & **Average** & **St. D** & **Average** & **St. D** & **Average** & **St. D** & **Average** & **St. D** & **Average** & **St. D** \\ \hline \multirow{10}{*}{**HV**} & Counter Strike & 0.2826 & 0.1162 & **0.7094** & **0.0129** & 0.6029 & 0.0234 & **0.6498** & **0.0113** & **0.7022** & **0.0165** \\ \cline{2-11}  & SPL SimulES, PnP & 0.2698 & 0.0954 & **0.6524** & **0.0150** & 0.4659 & 0.0314 & **0.5413** & **0.0176** & **0.6390** & **0.0155** \\ \cline{2-11}  & Smart Home v2.2 & 0.3460 & 0.1105 & **0.6885** & **0.0160** & 0.5742 & 0.0353 & **0.6052** & **0.0300** & **0.6843** & **0.0198** \\ \cline{2-11}  & Video Player & 0.3659 & 0.0969 & **0.6961** & **0.0155** & 0.5795 & 0.0108 & **0.6085** & **0.0262** & **0.6864** & **0.0193** \\ \cline{2-11}  & Model Transformation & 0.3600 & 0.0771 & **0.6009** & **0.0146** & 0.4677 & 0.0256 & **0.5276** & **0.0210** & **0.6136** & **0.0210** \\ \cline{2-11}  & Coche Ecologico & 0.3257 & 0.0802 & **0.4868** & **0.0048** & 0.3227 & 0.0107 & **0.4541** & **0.0329** & **0.5025** & **0.0013** \\ \hline \multirow{10}{*}{**Spacing**} & Counter Strike & 0.1138 & 0.0543 & **0.0329** & **0.0033** & 0.0496 & 0.0115 & **0.0316** & **0.0026** & **0.0157** & **0.0017** \\ \cline{2-11}  & SPL SimulES, PnP & 0.0713 & 0.0364 & **0.0208** & **0.0019** & 0.0521 & 0.0121 & **0.0295** & **0.0026** & **0.0113** & **0.0011** \\ \cline{1-1} \cline{2-11}  & Smart Home v2.2 & 0.0753 & 0.0330 & **0.0302** & **0.0024** & 0.0474 & 0.0102 & **0.0342** & **0.0037** & **0.0126** & **0.0086** \\ \cline{1-1} \cline{2-11}  & Video Player & 0.0855 & 0.0430 & **0.0296** & **0.0028** & 0.0502 & 0.0108 & **0.0328** & **0.0046** & **0.0127** & **0.0015** \\ \cline{1-1} \cline{2-11}  & Model Transformation & 0.0527 & 0.0112 & **0.0262** & **0.0021** & 0.0412 & 0.0081 & **0.0292** & **0.0024** & **0.0102** & **0.0008** \\ \cline{1-1} \cline{2-11}  & Coche Ecologico & 0.0417 & 0.0173 & **0.0217** & **0.0016** & 0.0720 & 0.0218 & **0.0179** & **0.0030** & **0.0095** & **0.0013** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Indicators of hypervolume (HV) and spacing values for four objectives.