## 1 Introduction
In the paper, we aim to demonstrate the performance of maintainability prediction models built on different amounts of historic measurements of software metrics. The goal was not to optimise the prediction models, but to research if additional historic software metric measurement data contribute to the its performance. In iterations of the experiment, we built software maintainability prediction models, whereby each iteration added a layer of software metrics measurements of precedingly released versions of a software project. In the study, we aimed to verify the performance of software maintainability prediction models improved by version-to-version changes in software metric measurements. To achieve our research goals, we followed an example of other similar studies in the field of software quality management, e.g., the study conducted by [PERSON] and [PERSON] [2]. Instead of relying on single release measurements of a software product, our approach considers a history data of software metric changes through time. As in the work of [PERSON] and [PERSON] [19], the maintainability of the software projects used in the study was assessed by the maintainability index. Given the above assumptions, we have raised the following research questions:  
RQ1: How do additional layers of software metric measurement data contribute to the performance of future software maintainability prediction? RQ2: Which software metrics from the C&K metric set have a strong impact on the performance of future software maintainability prediction? To answer the research questions, we experimented on open-source Java-based software projects with publicly available release history. Based on the related work, we assessed the internal quality of the software projects with the C&K metric set. Since the historic software metrics data measured on previous releases of software provide additional information to machine learning or statistical models,it is intuitive to assume that this additional information will contribute to more successful prediction models. To verify this assumption, software metric measurements extracted from preceding releases of software components were added to the maintainability prediction model gradually to verify whether they improved the prediction. The contributions of this paper are the following:  
* Application of machine learning techniques to future software maintainability prediction. * Investigation of the impact of historic software metrics measurement from previous releases on the future software maintainability prediction. * Investigation of the impact of individual C&K software metric changes between historic software releases on the future software maintainability prediction. The remainder of this paper is structured as follows. First, in Section 2, we start with an overview of the basic concepts regarding maintainability prediction models and software evolution analysis. Section 3 introduces a brief literature overview of related works. Next, we outline the data collection and preparation process in Section 4. Additionally, in Section 4 we describe the design of the experiment. The results of the research are displayed in Section 5. The discussion, including the threads to the validity of our research, is presented in Section 6. Section 7 concludes the paper and shows the direction for future work.