## 5 The Results of the Experiment
### Impact of Software Metrics-RQ2

Table 8: Descriptive statistic values of changes of software metrics grouped by the change of MINC. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CBO** & **DIT** & **LCOM** & **NSUB** & **RFC** & **TCC** \\ \hline N-0 & 3 & 5 & 4 & 6 & 2 & 1 \\ \hline N-1 & 2 & 6 & 3 & 1 & 5 & 4 \\ N-2 & 3 & 6 & 2 & 1 & 5 & 4 \\ N-4 & 6 & 1 & 5 & 2 & 3 & 4 \\ N-6 & 1 & 6 & 4 & 5 & 2 & 3 \\ N-8 & 2 & 5 & 4 & 6 & 3 & 1 \\ N-10 & 2 & 5 & 3 & 6 & 4 & 1 \\ N-12 & 2 &In each iteration, the impact (importance) of every software metric was evaluated with nine ranking methods, as was shown in Table 6. Every ranking method resulted in important factors for every software metric change between multiple consecutive software releases. This means that every iteration had multiple features from the same metric, with each feature for its version change. In this analysis, the multiple importance factors for every metric in each iteration were averaged, resulting in the average importance factor for every software metric in one iteration. The average importance factors of metrics were ranked and the ranks are presented in Table 9.  
According to the reference ranking presented in row N-0 of the Table 9, the metrics that contributed to the software maintainability prediction model the most were the software metrics TCC and RFC. On the other hand, software metrics with the weakest impact on the prediction model were the software metrics NSUB and DIT. The data analysis showed that, when the software maintainability prediction model is built based exclusively on change values in software metric measurements of the release N-1, the results of the metric ranking deviated greatly from the reference N-0 ranks. Namely, the software metrics with the strongest contribution to the prediction model were the metrics NSUB and CBO, the software metrics with the weakest contribution to the prediction model, on the other hand, were the metrics DIT and RFC. From the presented data, it can be observed that, adding software metrics measurement data iteratively to the prediction model, the ranks of contribution to the prediction model eventually converged toward the reference ranks for the software metric with the strongest and software metric with the weakest impact on the software maintainability prediction model. A non-parametric [PERSON]'s ANOVA test was used to test if the differences between ranks were statistically significant. This test was used as the ranks are ordinal, not scale type values, and a Shapiro-Wilk's test showed that these ranks do not conform to the normal distribution (\(p<0.001\)). The results of [PERSON]'s ANOVA showed that there were statistically significant differences between importance ranks of software metrics change in the impacts in contributing to the prediction of future MINC change (\(\chi^{2}\)(5)=17.66, p=0.003). Consequently, post-hoc analysis was used with a Durbin-Conover test with Holm-Bonferroni correction. The results of post-hoc tests are in Table 10.  
There are six pairwise comparisons between importance ranks of software metrics change, which are statistically significant: CBO had a higher impact than DIT (median ranks 2 vs. 5), CBO had a higher impact than NSUB (median ranks 2 vs. 6), RFC had a higher impact than DIT (median ranks 3 vs. 5), TCC had a higher impact than DIT (median ranks 1 vs. 5), TCC had a higher impact than LCOM (median ranks 1 vs. 4) and TCC had a higher impact than NSUB (median ranks 1 vs. 6).