Program slicing is a well-established technique for identifying a reduced subset of a program based on pre-defined criteria, leading to complexity reduction in subsequent activities. Despite extensive study over the past 40 years, slicing techniques for software product lines (SPLs) remain notably scarce. The absence of dedicated SPL slicing approaches hinders their efficient analysis and maintenance, limiting the ability to focus only on relevant parts of the SPL. One reason for this deficiency is the complex nature of a common variability implementation: the use of C preprocessor #ifdef -annotations within C code. A slicing approach for C-based SPLs must address the intricate interplay between the C code and the functionality introduced by the C preprocessor. Effectively handling these intricacies will unleash the full potential of SPL analysis. In this paper, we present a novel syntax-preserving program slicing approach for C-based SPLs. Unlike existing methods, our approach enables the computation of program slices through an integrated analysis of both C and CPP code, while preserving the original program syntax (no element of its syntax is disregarded or changed). This preservation ensures that the resulting program slices remain authentic subsets of the SPL, making them suitable inputs for variability-aware analyses. Additionally, we demonstrate the practical applicability of these slices in the context of software transplantation, showcasing their potential for facilitating functionality transfer between different program versions. In contrast to existing transplantation approaches, our solution works without test cases, removing the need for product configuration and execution. Consequently, the variability implementation (along with all other contained preprocessor code) is preserved during the transplantation. We empirically evaluate our approach on four distinct open-source SPLs, showcasing its effectiveness in generating diverse program slices tailored to different slicing criteria. We asses the accuracy of our code representation, the time required for slicing and transplantation, the size reduction achieved through the slices, and the functionality of our variability-aware transplantation approach. • A novel, variability-aware, and syntax-preserving code representation designed for program slicing. • A fully automated slicing approach for calculating exhaustive slices of C-based SPLs. • A practical demonstration of automated software transplantation using slices of C-based SPLs.

A Self-adaptive System (SaS) modifies its domain functionality according to changes in its environment autonomously. Distributed control and central control represent two complementary paradigms to establish this capability. The selection of one of them leads to significant trade-offs regarding certain software qualities when designing a SaS. A promising approach to minimize these trade-offs is an integration, which combines the individual benefits to achieve the best of both paradigms. However, establishing such a multi-paradigm control requires comprehensive knowledge about control options and their interactions, which is hardly available.In this paper, we present patterns for integrating distributed and central control. We introduce them by a schema of Control Action Types (CATs). Each CAT describes a unique type of interaction between a central controller and a distributed controlled SaS to achieve a desired adaptation. Further, we identify involved tradeoffs between these CATs aiming at a systematic discussion of the range of multi-paradigm control for a SaS.

Exploring an unfamiliar large-scale software system is challenging, especially when based solely on source code. While software visualizations help in gaining an overview of a system, they generally neglect architecture knowledge in their representations, e.g., by arranging elements along package structures rather than functional components or locking users in a specific abstraction only slightly above the source code. In this paper, we introduce an automated approach for software architecture recovery and use its results in an immersive 3D virtual reality software visualization to aid accessing and relating architecture knowledge. We further provide a semantic zoom that allows a user to access and relate information both horizontally on the same abstraction level, e.g., by following method calls, and vertically across different abstraction levels, e.g., from a class to its containing component. We evaluate our contribution in a controlled experiment contrasting the usefulness regarding software exploration and comprehension of our concepts with those of the established CityVR visualization and the Eclipse IDE.

Abstract Verification approaches for Software Product Lines (SPL) aim at detecting variability-related defects and inconsistencies. In general, these analyses take a significant amount of time to provide complete results for an entire, complex SPL. If the SPL evolves, these results potentially become invalid, which requires a time-consuming re-verification of the entire SPL for each increment. However, in previous work we showed that variability-related changes occur rather infrequently and typically only affect small parts of a SPL. In this paper, we utilize this observation and present an incremental dead variable code analysis as an example for incremental SPL verification, which achieves significant performance improvements. It explicitly considers changes and partially updates its previous results by re-verifying changed artifacts only. We apply this approach to the Linux kernel demonstrating that our fastest incremental strategy takes only 3.20 seconds or less for most of the changes, while the non-incremental approach takes 1,020 seconds in median. We also discuss the impact of different variants of our strategy on the overall performance, providing insights into optimizations that are worthwhile.

The evolution of a Software Product Line (SPL) typically affects a variety of artifact types. The intensity (the frequency and the amount) in which developers change variability information in these different types of artifacts is currently unknown. In this paper, we present a fine-grained approach for the variability-centric extraction and analysis of changes to code, build, and variability model artifacts introduced by commits. This approach complements existing work that is typically based on a feature-perspective and, thus, abstracts from this level of detail. Further, it provides a detailed understanding of the intensity of changes affecting variability information in these types of artifacts. We apply our approach to the Linux kernel revealing that changes to variability information occur infrequently and only affect small parts of the analyzed artifacts. Further, we outline how these results may improve certain analysis and verification tasks during SPL evolution.