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. To remedy this problem 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 describe how FEVER works, and apply it to several releases of the Linux kernel. Our evaluation on 300 randomly selected commits, from two different releases, shows our results are accurate in 82.6% of the commits. Furthermore, we illustrate how the populated FEVER graph database thus obtained can be used in typical Linux engineering tasks.

Recent studies have shown that the violation of the Interface Segregation Principle (ISP) is critical for maintaining and evolving software systems. Fat interfaces (i.e., interfaces violating the ISP) change more frequently and degrade the quality of the components coupled to them. According to the ISP the interfaces' design should force no client to depend on methods it does not invoke. Fat interfaces should be split into smaller interfaces exposing only the methods invoked by groups of clients. However, applying the ISP is a challenging task when fat interfaces are invoked differently by many clients. In this paper, we formulate the problem of applying the ISP as a multi-objective clustering problem and we propose a genetic algorithm to solve it. We evaluate the capability of the proposed genetic algorithm with 42,318 public Java APIs whose clients' usage has been mined from the Maven repository. The results of this study show that the genetic algorithm outperforms other search based approaches (i.e., random and simulated annealing approaches) in splitting the APIs according to the ISP.