## 5 The Results of the Experiment
### Impact of Historical Changes in Software Metric Measurements-RQ1
The analysis of the collected data shows that both generated prediction models came to more or less similar results in the final iteration of building the prediction models. Despite that, the random forest-based prediction model showed slightly better results in the final iteration. In general, the results indicate that the software maintainability prediction models showed better performance for both used classification algorithms when the model was enriched by historic measurement data extracted from prior releases. The analysis of the results shows that classification accuracy gains between the first and the final iteration of the prediction model used in the study were 0.008 in the case of CART, and 0.01 in the case of Random Forest being applied as the learner. Between the initial and the final iteration of the prediction model, the F-score performance indicator gained 0.095 percentage points (pp) in the case of CART and 0.117 pp in the case of random forest. From the viewpoint of the F-score classification metric, the data of the analysis shows that not every layer of historical measurement data added to the prediction model contributes to the performance gain of the model. The analysis of collected data shows that performance indicators of prediction models do not necessarily improve in every iteration of adding additional historic measurement data. This holds for both learners applied in the experiment. The charts of improvement of software maintainability prediction by the gradual addition of historic measurement data to the prediction models are depicted in Figure 7.  
Despite occasional declines in the prediction model performance, overall, the software maintainability prediction model gained in performance when enriched by data from the previous releases, as is shown with logarithmic trends in Figure 7. The accuracy trend for CART shows that the accuracy metric improved 0.001 in each iteration (this explains 60.92% of the variation of the metrics through the iterations, as is shown here with R\({}^{2}\) = 0.6092), while the accuracy for random forest improved 0.0011 in each iteration (R\({}^{2}\) = 0.7111). Despite slight improvements in the accuracy, more drastic improvements can be seen in the F-score metric. An upwards linear trend is evident for CART, where F-score improved 0.0091 in each iteration (R\({}^{2}\) = 0.9270), and also in random forest, where it improved by 0.0115 when adding historical software metric changes (R\({}^{2}\) = 0.9693), both explaining more than 90% of the variance. The high variance explained with the logarithmic trend, especially for the F-score metric, is in line with assumptions that there is a slow decline in the usefulness of historical changes further in the past. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CART**} & \multicolumn{3}{c}{**Random Forest**} \\ \cline{2-9}  & **CA** & \(\Delta\)**CA** & **F-Score** & \(\Delta\)**F-Score** & **CA** & \(\Delta\)**CA** & **F-Score** & \(\Delta\)**F-Score** \\ \hline N-1 & 0.570 & - & 0.436 & - & 0.570 & - & 0.436 & - \\ N-2 & 0.567 & \(-\)0.003 & 0.458 & 0.022 & 0.573 & 0.003 & 0.468 & 0.032 \\ N-4 & 0.573 & 0.006 & 0.502 & 0.044 & 0.579 & 0.006 & 0.511 & 0.043