## 5 Results and Data Analysis
### RQ2.1: Maintainability Measurement in Different Software Systems  
The scatter plot matrix representation with pairwise correlation analysis is shown in Figure 5. Based on the results of the normality tests reported in Table 9, the Pearson  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Comparison** & **Reference** & **Shapiro–Wilk** & **Avg. (\(\pm\)SE a/Med. b** & **Test t*/Z b** & **Statistical** & **Effect Size** \\
**Group** & **Group** & **Normality Test** & **Difference** & **Value** & **Significance** & \(\mathbf{d^{\,3}\!/_{r_{c}}}\)b \\ \hline \(Ml_{orig. }\) & \(Ml_{orig. (CM)}\) & W(45) = 0.71, \(p<0.001\)b & 48.50 & \(Z=-5.841\) & \(p<  
Correlation Coefficient was used to analyze the relationships between the Index variants. The results showed that all Index variants are strongly or very strongly positively correlated. The most outliers, represented by blue dots that are furthest from the regression line, can be observed when assessing the relationship between an Index variant without and one with the comment part. That is why, for example, the correlation between \(M1_{LOC}\) and \(M1_{JH(CM)}\) (\(r=0.896\)) is lower compared to the correlation between \(M1_{LOC}\) and \(M1_{impr. }\) (\(r=0.997\)). To further analyze the multivariate relationships based on the Pearson Correlation Coefficient between the seven Index variants, multidimensional scaling was used to reduce the dimensions of the data to two. The resulting visualization seen in Figure 6 attempts to model the similarity and dissimilarity between the Index variants as the distances they  
Figure 5: Scatter plot matrix representing pairwise correlation analyses (all reported correlations are significant (\(p<0.001\))) and variation in the data distribution. exhibit in the vector space. From the visualization, it can be observed that the Index variants with the comment part, namely \(M_{orig. (CM)}\), \(M_{Impr. (CM)}\), and \(M_{IJH(CM)}\), appear to be close together on the left side of the plot, while the Index variants without the comment part, namely \(M_{orig}\), \(M_{Impr. }\), \(M_{LOC}\), and \(M_{IJH}\), appear close together on the right side of the plot. This observation indicates that Index variants without the comment part are more similar to each other in comparison to Index variants with the comment part. Similarly, this observation indicates that Index variants with the comment part are more similar to each other in comparison to Index variants without the comment part. The identified two groups are represented by the areas in the figure, which were manually annotated to emphasize the clear distinction between the two groups, as well as the close proximity of the Index variants within each group. Instead of the manual identification of groups, to more accurately determine the similar groups of the Index variants, clustering on the Pearson Correlation Coefficients was performed. In Figure 7, a correlation heat map with a dendrogram that depicts the results of hierarchical clustering is presented. The dendrogram displays which Index variants are most similar to each other. The results showed that the first cluster created includes \(M_{Impr. }\) and \(M_{LOC}\). The second cluster contains \(M_{Impr. (CM)}\) and \(M_{IJH(CM)}\), which is in the next step joined with \(M_{orig.(CM)}\). The fourth cluster includes \(M_{orig. }\) and \(M_{IJH}\), which was in the next step combined with the first cluster of \(M_{Impr. }\) and \(M_{ILOC}\). Lastly, the two remaining clusters were merged into one. However, it can be noted that the merge threshold level of the two final clusters, as indicated by the distance in the dendrogram, is significantly higher compared to the previous clustering steps. These two distinct clusters, as identified by the hierarchical clustering, are the same as those that were manually identified earlier, i.e., the Index variants with the comment part and the Index variants without the comment part.