The evolution of highly configurable systems is known to be a challenging task. Thorough understanding of configuration options their relationships, and their implementation in various types of artefacts (variability model, mapping, and implementation) is required to avoid compilation errors, invalid products, or dead code. Recent studies focusing on co-evolution of artefacts detailed feature-oriented change scenarios, describing how related artefacts might change over time. However, relying on manual analysis of commits, such work do not provide the means to obtain quantitative information on the frequency of described scenarios nor information on the exhaustiveness of the presented scenarios for the evolution of a large scale system. In this work, we propose FEVER and its instantiation for the Linux kernel. FEVER extracts detailed information on changes in variability models (KConfig files), assets (preprocessor based C code), and mappings (Makefiles). We apply this methodology to the Linux kernel and build a dataset comprised of 15 releases of the kernel history. We performed an evaluation of the FEVER approach by manually inspecting the data and compared it with commits in the system’s history. The evaluation shows that FEVER accurately captures feature related changes for more than 85% of the 810 manually inspected commits. We use the collected data to reflect on occurrences of co-evolution in practice. Our analysis shows that complex co-evolution scenarios occur in every studied release but are not among the most frequent change scenarios, as they only occur for 8 to 13% of the evolving features. Moreover, only a minority of developers working on a given release will make changes to all artefacts related to a feature (between 10% and 13% of authors). While our conclusions are derived from observations on the evolution of the Linux kernel, we believe that they may have implications for tool developers as well as guide further research in the field of co-evolution of artefacts.

Evolving a large scale, highly variable system is a challenging task. For such a system, evolution operations often require to update consistently both their implementation and its feature model. In this context, the evolution of the feature model closely follows the evolution of the system. The purpose of this work is to show that fine-grained feature changes can be used to guide the evolution of the highly variable system. In this paper, we present an approach to obtain fine-grained feature model changes with its supporting tool “FMDiff”. Our approach is tailored for Kconfig-based variability models and proposes a feature change classification detailing changes in features, their attributes and attribute values. We apply our approach to the Linux kernel feature model, extracting feature changes occurring in sixteen official releases. In contrast to previous studies, we found that feature modifications are responsible for most of the changes. Then, by taking advantage of the multi-platform aspect of the Linux kernel, we observe the effects of a feature change across the different architecture-specific feature models of the kernel. We found that between 10 and 50 % of feature changes impact all the architecture-specific feature models, offering a new perspective on studies of the evolution of the Linux feature model and development practices of its developers.

The study of the evolution of highly configurable systems requires a thorough understanding of thee core ingredients of such systems: (1) the underlying variability model; (2) the assets that together implement the configurable features; and (3) the mapping from variable features to actual assets. Unfortunately, to date no systematic way to obtain such information at a sufficiently fine grained level exists.

Despite a global recognition of the problem, and massive investment from researchers and practitioners, the evolution of complex software systems is still a major challenge for today's architects and developers. In the context of product lines, or highly configurable systems, variability in the implementation and design makes many of the pre-existing challenges even more difficult to tackle. Many approaches and tools have been designed, but developers still miss the tools and methods enabling safe evolution of complex, variable systems. In this paper, we present our research plans toward this goal: making the evolution of software product lines safer. We show, by use of two concrete examples of changes that occurred in Linux, that simple heuristics can be applied to facilitate change comprehension and avoid common mistakes, without relying on heavy tooling. Based on those observations, we present the steps we intend to take to build a framework to regroup and classify changes, run simple checks, and eventually increase the quality of code deliveries affecting the variability model, mapping and implementation of software product lines.