## 6 Discussion  
In this section, we discuss additional observations obtained through the result of the HS-based parameter optimization we performed.  
The first is the relevance of the search space we designed. In our correlation matrix analysis conducted in RQ3, class_weight was an important parameter which had a weak positive correlation. On the other hand, since min_impurity has a negative correlation, it is a parameter that does not help to improve performance. Thus, it has become a guide to reducing prediction performance. We recommend that the search space can be effectively designed through the correlation analysis between the harmony and fitness values recorded in HM. In our experiment, changing class imbalanced ratio and class_weight of decision tree influenced the performance of prediction.  
We also analyzed the impact of the performance by the presence or absence of a bagging classifier. We compared the performance results in RQ1 with the optimization using only the decision tree classifier. The results are summarized in Table 10. The best performance values are indicated in bold face and significant differences beyond medium size were underlined in the table. They showed that bagging increases PD and B, decreases PF, with significant differences. The bagging classifier is known as good meta-learner to prevent overfitting and reduce variance [42; 43; 44; 45]. [PERSON] et al. [46] reported that they compare four base classifiers and four ensemble classification methods, but they did not find significant differences between the two. They mentioned that ensemble techniques should be able to improve the model performance with respect to single classifiers, but they concluded that ensemble techniques do not always guarantee better performance with respect to a single classifier in their work. In our experiment, the presence or absence of bagging had a significant effect on the performance improvement. Our dataset had unique characteristics that reflect practice in 2 maritime and 6 open source projects, and [PERSON] et al. had dataset characteristics that reflect practice in 14 mobile app projects. In the two studies, because contexts such as dataset, prediction model, and development environment are different from each other, the results seem to be different [47; 48].  
Finally, a comparison of performance improvement was made between maritime domain software and open source software. Both groups showed improvement in performance, but the baseline of maritime domain software already had high performance, so there was a limit to improve it. In our experiment in RQ1 varying class imbalance ratio, we observed significant performance improvement with high class imbalance datasets. On the other hand, in experiment RQ4, open source software showed a relatively high effect of performance improvement.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**DT**} & \multicolumn{3}{c}{**DT+BG**} & \multicolumn{3}{c}{**DT+BG+HS**} & \multicolumn{3}{c}{**[PERSON] et al. [7]**} \\ \cline{2-11}  & **PD** & **PF** & **B** & **PD** & **PF** & **B** & **PD** & **PF** & **B** & **PD** & **PF** & **B** \\ \hline BU & 0.571 & 0.147 & 0.679 & 0.571 & 0.147 & 0.679 & **0.748** & 0.287 & **0.729** & 0.69 & 0.347 & 0.671 \\ CO & 0.503 & 0.244 & 0.608 & 0.468 & 0.111 & 0.616 & **0.750** & 0.322 & **0.711** & 0.67 & 0.272 & 0.698 \\ JD & 0.305 & 0.127 & 0.500 & 0.184 & 0.023 & 0.423 & **0.661** & 0.278 & **0.690** & 0.65 & 0.329 & 0.660 \\ PL & 0.217 & 0.049 & 0.445 & 0.094 & 0.005 & 0.359 & **0.747** & 0.304 & **0.720** & 0.70 & 0.304 & 0.698 \\ MO & 0.335 & 0.126 & 0.521 & 0.230 & 0.027 & 0.455 & **0.716** & 0.200 & **0.754** & 0.63 & 0.224 & 0.694 \\ PO & 0.475 & 0.181 & 0.607 & 0.451 & 0.076 & 0.608 & **0.730** & 0.241 & **0.744** & 0.65 & 0.191 & 0.718 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Experimental results to answer RQ4.