## 7 Conclusions  
The study explores the impact of historical software version changes in predicting the future maintainability of the software. The novelty of our study is, unlike related works in the research field, a detailed analysis and assessment of the metric measurement contribution to maintainability prediction performance on a version-to-version basis, which to our best knowledge has not been evaluated previously. In the experiment, 40 real-life open-sourced Java-based software components published in the Maven Repository were included, for which the history of 19 successive releases was available. The internal quality of collected software projects was assessed using the C&K metric set, and the software maintainability of the software projects was assessed by the maintainability index. With these data, the software maintainability prediction models were built and assessed through two performance indicators, classification accuracy and F-score. The outcome of the experiment was analysed with statistical tests to get the answer to our research question if historical changes in software have an impact on the future software maintainability. The key finding of the study was:  
* In the context of RQ1, our results showed that despite an occasional smaller decline in the performance of the software maintainability change prediction model during the experiment, in general, the prediction models gained on the performance by every layer of newly added software metrics measurement data
* With regard to RQ2, 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 of the software metrics according to the observations of related research. The results of the data analysis show that the gain of software maintainability prediction performance was the greatest when the prediction model was enriched by software metric measurements of versions closer to the latest release. Moreover, from the newest release, more distant versions of a software project contributed less to the performance of the maintainability prediction model. The main contribution of the paper is to show the prediction model's performance through the iterations of the experiment grows with the logarithmic trend. Therefore, the measurement data of the nearby versions of the observed release of a software project remain relevant for the building of maintainabilityprediction models. Additionally, the paper evaluates the contribution of the growth of the prediction model's performance through the iterations. How many versions will be considered in the assessment depends on a compromise between the gain in prediction performance and measurement data to be collected. Additionally, we tested the validity of our approach and its compliance with existing research by conducting a feature importance calculation and ranking. It was shown that, when adding more historical releases to the analysis, the importance of software metrics in predicting the maintainability change resembled the importance of software metrics in calculating the maintainability in the same version. As we are dealing in predicting the future trend of maintainability change, for which the software metrics are not yet known (as with existing research), this is also an important finding. First, the results obtained in the study could have an impact on software maintainability assessment in software development environments, where a smooth evolution of software products is one of the key risks that have to be managed. Enhanced software maintainability prediction models shift the focus of the maintainability assessment of the latest release of a software component. Instead, the dynamics of software maintainability change in a software product is assessed through a temporal perspective. Without intervention, the patterns of change in the maintainability from the past are likely to continue in the future.