Most modern software systems can be adjusted to satisfy sets of conflicting requirements issued by different groups of users, based on their intended usage or execution context. For systems where configurations are a core concern, specific implementation mechanisms are put in place to allow the instantiation of sets of tailored components. Among those, we find selection processes for code artefacts, variability-related annotation in the code, variability models representing the available features and their allowed combina- tions.

In such systems, features, or units of variability, are scattered across the aforementioned types of artefacts. Maintenance and enhancement of existing systems remain a challenge today, for all types of software systems. But in the case of variant-rich systems, engineers face an additional challenge due to the complexity of the product instantiation mechanisms: the maintenance of the variability model of the system, the complex build mechanisms, and fine-grained variability in the source code. The evolution of the system should be performed such that the information contained within the various artefacts remains consistent. In practice, this means that as the implementation of the system evolves, so should the mechanisms put in place to generate tailored products.

Little information is available regarding changes occurring in such systems. To efficiently support such developers tasks and ease maintenance and enhancements activities, we need a deep understanding of the changes that take place in such systems. The state of the art provides trends over long period of times, highlighting systems growth - such as number of added or removed features in each release, or the evolution of cross- tree constraints in a variability model. While important to describe the core dynamics behind the evolution of a system, this does not provide information on the changes per- formed by developers leading to such trends. Similarly, this global information cannot be leveraged to facilitate developers’ activities.

The focus of this thesis is the acquisition and usage of change information regard- ing variant-rich system evolution. We show how the information lacking from today’s state-of-the-art can be obtained from variant-rich system change history. We propose a set of tool-supported approaches designed to gather such information and show how we leverage change information for change impact analysis, or to derive knowledge on developer practices and the challenges they face during such operations. With this work, we shed new light on change scenarios involving heterogeneous artefacts regarding their nature as well as their prevalence in the evolution of such complex systems, and change impact analysis in variant-rich systems.

We designed a model-based approach to extract feature related changes in heterogeneous artefacts. With such an approach, we can gather detailed information of feature evolution in all relevant artefacts. We created an approach for multi-product line modelling for impact computation. We leverage variability information to produce a collection of inter-related variability models, and show how to use it for targeted feature-

change impact analysis on available capabilities of the product family. By applying our change extraction approaches on the Linux kernel, we were able to empirically characterise the evolution of the variability model of this system. We showed that the variability model of that system evolves mostly through modification of existing features, rather than through additions and removals. Similarly, we studied co-evolution of artefacts dur- ing feature evolution in the Linux kernel. Our study revealed that, in this system, most features evolve mostly through their implementation, and complex changes, involving heterogeneous artefacts are not the most frequent.
Through this work, we provide detailed information on the evolution of a system, namely the Linux kernel, and the means used to obtain this information. We show that the gathered data allow us to reflect on the evolution of such a system, and we argue that gathering such information on any system is a source of valuable information regarding a system architecture. To this end, all tools developed in the context of this study were made available to the public.
In this work, we provide key information on the evolution of the Linux kernel, as well as the means to obtain the same information from other variant-rich systems. The knowledge gained on common evolution scenarios is critical for tool developers focus- ing on the support of development of variant-rich systems. A better understanding of common evolution scenario also allows engineers to design systems that will be better equipped to elegantly evolve through such scenarios. While a number of challenges will still have to be addressed in this domain, this work constitutes a step toward a better understanding of variant-rich system evolution and therefore toward better variant-rich system designs.