## 6 Discussion  
The results of the study indicate that software maintainability models enriched by historic measurement data achieve better results in predicting future maintainability change compared to the model built with measurement of one release of a software project. The results of the experiment show that the software maintainability prediction models enriched by historic measurements perform better for both performance indicators used in the study, the classification accuracy and F-score. In general, the prediction model performance gain was slightly more distinct in the case of the F-score performance indicator. This is a relevant fact, since F-score is a more balanced metric, where the performance of predicting (classifying) every class is more important than just general accuracy, even when some classes are ignored [70]. The results of the study show that the prediction model based on the measurements obtained from a single release of a software project is, from the perspective of F-score performance indicator, quite weak. In the experiment, both performance indicators were improving throughout iterations after the model was enhanced by additional historical releases of the studied software project.  
An important domain of software maintainability research represents studies of links between software metrics and the maintainability of software projects. The systematic literature review conducted by [PERSON] et al. [58] implied a strong link between the complexity metrics and the maintainability. On the other hand, the measures of inheritance aspects of software design manifest a weaker link to the maintainability. Among the software metrics used in our study, TCC metrics had the strongest contribution to the accuracy of the prediction model, which was used as an equivalent of the WMC software metric of the C&K set. On the other hand, the weakest contribution to the prediction model performance was, on average, measured by the DIT and NSUB software metrics, an equivalent of the NOC metric in C&K. An interesting observation is that the profile of contributions of software metrics in our study is getting closer to the contribution profile of metrics observed by [PERSON] et al. in the last iterations of the experiment when the series of history measurement data was added to the prediction model. In our study, the contribution of measures based on a single software release in the first iteration deviates strongly from the observations of the studies of other authors. Eventually, after adding extra layers of historic software metric measurements, the contribution of the metrics approached the results of the mentioned research.  
The profile of software metrics' contribution to the prediction models shows that the maintainability index, applied in the same way as in this research, can be, despite some criticisms regarding computation and applicability to the object-oriented paradigm [34; 71], used as an efficient predictor of the future software maintainability change. Namely, the strong link between complexity measures and the maintainability, and the weak link between software inheritance and the maintainability, indicates the eligibility of the maintainability index formula.  
According to the results of the study, the largest contribution to the performance of the prediction model was by the measurement data collected from releases closer to the latest release of a software project, namely, newer versions in the history of the software project's releases. This is indicated by the logarithmic trend in the performance growth revealed by the analysis of the collected data in the experiment. The analysis of the data did show that additional layers of measurement data added in individual iterations of the experiment contributed to the prediction ability of the prediction model. On average, by any layer of the additional historic measurement data, the model was getting more successful.