Research on open-source software evolution gained popularity in the last decade focusing on the theoretical determining factors. Additional works studied growth patterns modeling using time series techniques on small projects and metrics samples or non-openly available larger datasets. Limitations in reproducibility and scalability of these methodologies add to the lack of research on time series methodologies applied to open-source software evolution. Thus, time series approaches from different domains are needed to address the multivariate nature of larger and variable samples of open-source projects and metrics time series data. This thesis aims to provide a reproducible and scalable framework to support researchers in studying open-source software evolution using patterns modeling, time series merging, multivariate time series clustering and multivariate time series forecasting. An openly available dataset of 1328 projects is built using relevant metrics extracted from a systematic literature review. The metrics time series are segmented and clustered to obtain generalized growth patterns: Steep; Shallow; Plateau. The sequence of patterns and their correlation are used to create three project clusters, from which prediction models for all metrics are trained to perform multivariate time series forecasting. Experiment results give confidence over the reproducibility and the scalability of the framework and show how the pattern shifts can be linked to real events in projects' histories. The thesis provides an additional perspective on open-source software evolution and can serve as a starting point for further studies.

The study of bugs can provide important information to understand their nature in the context of complex software systems as well as supporting developers in their detection, fix and prevention. Previous studies focused on analyzing bugs under different perspectives such as changes at code level, frequency, semantics, symptoms, root causes and reproducibility through test cases. Although these studies offer valid methodologies that can be applied in different areas of bugs analysis, literature suggests that very little focus has been aimed towards configuration management systems.

This research has the goal to provide a characterization of bugs in the Moby configuration management system, an open framework used to create container systems. A random sample of 100 collected Moby bugs is manually inspected and categorized by their (1) symptoms, (2) root causes, (3) impact, (4) fixes, (5) system dependency and (6) triggers. Some representative takeaways suggest that: bugs symptoms are mostly linked to a specific root cause, which means that bugs occur in certain areas of the system; the modular nature of Moby drives up the criticality of the bugs as each component needs to work correctly; bugs are overall hard to reproduce as only 24.5\% of the fixes included a test case; developing an automatic tool that provides a historical distribution of bugs can support maintainers in their work by enhancing bugs prevention.

The research provides an analysis and categorization of bugs in the Moby configuration management system. The work adds a new perspective to the literature on the topics of both bugs analysis and configuration management systems and can be used as a starting point for further studies.