IIoT, Industry 4.0 or CPPS software platforms are cornerstones of smart manufacturing production systems. Such platforms integrate machines, IIoT and edge devices, realize distributed (management) functionality and provide the basis for user-defined IIoT applications. Individual instances in research and industrial practice do share commonalities while they also differ significantly. A detailed overview of the platform landscape is fundamental for innovative research. However, actual surveys and literature reviews concentrate on specific aspects and usually focus only on the research works, neglecting specific aspects of the industrial use of IIoT platforms. We aim at a systematic overview of the functionalities and approaches of scientific and industrial IIoT platforms along 16 analysis dimensions and thereby exposing gaps between the focuses of research on IIoT platforms and actual industrial IIoT platforms in use. By doing so we are able to highlight future areas of interest to research as well as indicating potentially over-researched areas which are of less interest in actual industrial IIoT platforms. We combine a systematic literature review of scientific IIoT platform research with a systematic analysis of industrial IIoT platforms. We start off with 1620 research papers plus 70 from snowballing that we systematically filter down to 36 papers (plus 11 added by a SLR update) providing sufficient information for a data extraction, which we analyze along 16 topics to extract actual capabilities and differences of relevant platform approaches. In a second step, we contrast these results with an analysis of 21 industrial platforms. Similar approaches, differences and topics for future are exhibited. In comparison with 21 industrial platforms along the same analysis topics, we distill various commonalities, differences, trends and gaps. • Systematic literature review of 36 IIoT and Industry 4.0 software platforms (total input including snowballing: 1620 paper candidates). • Outlook on recent publications by a systematic update of the SLR based on more than 337 papers identified by forward snowballing leading to further 11 relevant papers. • Analysis of the platforms with regard to 16 topics. • Systematic comparison with state of the practice with regard to 21 industrial platforms along 14 topics. • Identification of commonalities and gaps.

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.

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.

KernelHaven is an open infrastructure for Software Product Line (SPL) analysis. It is intended both as a production-quality analysis tool set as well as a research support tool, e.g., to support researchers in systematically exploring research hypothesis. For flexibility and ease of experimentation KernelHaven components are plug-ins for extracting certain information from SPL artifacts and processing this information, e.g., to check the correctness and consistency of variability information or to apply metrics. A configuration-based setup along with automatic documentation functionality allows different experiments and supports their easy reproduction.

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.

EASy-Producer is an open-source research toolset for engineering product lines, variability-rich software ecosystems, and dynamic software product lines. In this tutorial, we will focus on its model-based development and implementation capabilities, which are realized by the Variability Instantiation Language (VIL) and the Variability Template Language (VTL). Further, we will provide a basic introduction into the Integrated Variability Modeling Language (IVML) in order to use the provided information of IVML variability models and configurations during instantiation defined with VIL and as part of the templates created with VTL.

EASy-Producer is an open-source research toolset for engineering product lines, variability-rich software ecosystems, and dynamic software product lines. In this tutorial, we will focus on its (textual) variability modeling capabilities as well as its configuration and validation functionality. Further, we will provide an outlook on how EASy-Producer can be applied to variability instantiation.