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.

Asset Administration Shells are trending in Industry 4.0. In February 2024, the Industrial Digital Twin Association announced 84 and released 18 AAS sub model specifications. As an enabler on programming level, dedicated APIs are needed, for which, at this level of scale, automated creation is desirable. In this paper, we present a model-driven approach, which transforms extracted information from IDTA specifications into an intermediary meta-model and, from there, generates API code and tests. We show we can process all current IDTA specifications successfully leading in total to more than 50000 lines of code. However, syntactical variations and issues in the specifications impose obstacles that require human intervention or AI support. We also discuss experiences that we made and lessons learned.

Abstract Container-based virtualization is a popular technique, e.g., to realize microservice architectures. Recently, containers became popular in Industry 4.0 / IIoT systems, which typically consist of hundreds of (edge) devices and machines. In such setups, efficient management of containers is essential as offered by container orchestrators like Kubernetes. However, currently no specific overviews discussing orchestrator capabilities for Industry 4.0 are available. In this paper, we analyze nine open source container orchestrators for their application in Industry 4.0 or IIoT settings as a basis for future research and development. We contribute a systematic literature review to identify 23 basic orchestration requirements. We complement this by insights from an intensive requirements collection in a research project on intelligent industrial production, as well as selected features from a published generic orchestrator analysis. From these 66 requirements, we derive a requirements/feature taxonomy, which we use to analyze the nine open source orchestrators including Kubernetes. We show that there is, e.g., still a lack of support regarding edge devices, IIoT protocols, security mechanisms, and specialized resources for artificial intelligence.

The development of intelligent solutions for manufacturing is a challenging task. Industry 4.0 platforms can provide a unifying layer here. However, flexible AI support, openness for evolving service and components from different vendors and adaptability to the diverse and changing requirements is required from such a platform to boost IIoT development. For this purpose, our approach combines - as a "power trio" - (1) wide use of Asset Administration Shells (AAS) for targeting device, component and service heterogeneity, with (2) configuration support for dealing with the diverse and changing requirements and (3) code generation for cost-effective creation of customer specific platform instances, AAS and AI-based Industry 4.0 applications on top of the IIP-Ecosphere platform. The platform has been implemented based on vertically scaled AAS and evaluated with two Industry 4.0 demonstrators. In this context, we discuss the experiences we made with our approach.

Cyber-Physical Production Systems (CPPSs) are envisioned as next-generation adaptive production systems combining modern production techniques with the latest information technology. A CPPS creates a complex environment between different domains (mechanical, electrical, software engineering), requiring multidisciplinary solutions to tackle growing complexity issues and reduce (maintenance) effort. Software plays an increasingly important role in assuring an effective and efficient operation of CPPSs. However, software engineering methods applied for CPPSs seem to lag behind modern software engineering methods, where tremendous progress has been made in the last years. We initiated the Software Engineering in Cyber-Physical Production Systems Workshop (SECPPS-WS) to analyze and overcome this gap. After two instances with mostly academic participants, we conducted a full-day workshop with nine industry representatives from eight companies that develop and maintain CPPSs. Each industry representative presented their current work and challenges. We collected these challenges and condensed a categorized list of challenges backed by industry statements and literature. This paper presents the resulting list and pointers to (partial) solutions to offer guidance for academia and identify promising research opportunities in this area.

Monitoring complex distributed and heterogeneous systems such as cyber-physical or internet-of-things systems is challenging. Different kinds of information must be aggregated in a flexible manner to provide an overview on the actual state of the system. Recently, even heterogeneous Big Data systems have been proposed, which integrate classical server machines and hardware co-processors, such as Graphical Processing Units. In this paper, we present a system-level monitoring approach for a heterogeneous Big Data streaming system. We discuss the requirements stemming from a recent research project, a solution architecture and discuss experiences and promising results from preliminary experiments with the realization.

In recent years, the development of systems for processing and analyzing large amounts of data (so-called Big Data) has become an important sub-discipline of software engineering. However, to date there exits no comprehensive summary of the specific idiosyncrasies and challenges that the development of Big Data systems imposes on software engineers. With this paper, we aim to provide a first step towards filling this gap based on our collective experience from industry and academic projects as well as from consulting and initial literature reviews. The main contribution of our work is a concise summary of 26 challenges in engineering Big Data systems, collected and consolidated by means of a systematic identification process. The aim is to make practitioners more aware of common challenges and to offer researchers a solid baseline for identifying novel software engineering research directions.

Replicating software performance experiments is difficult. A common obstacle to replication is that recreating the hardware and software environments is often impractical. As researchers usually run their experiments on the hardware and software that happens to be available to them, recreating the experiments would require obtaining identical hardware, which can lead to high costs. Recreating the software environment is also difficult, as software components such as particular library versions might no longer be available. Cheap, standardized hardware components like the Raspberry Pi and portable software containers like the ones provided by Docker are a potential solution to meet the challenge of replicability. In this paper, we report on experiences from replicating performance experiments on Raspberry Pi devices with and without Docker and show that good replication results can be achieved for microbenchmarks such as JMH. Replication of macrobenchmarks like SPECjEnterprise 2010 proves to be much more difficult, as they are strongly affected by (non-standardized) peripherals. Inspired by previous microbenchmarking experiments on the Pi platform, we furthermore report on a systematic analysis of response time fluctuations, and present lessons learned on dos and don»ts for replicable performance experiments.

Creating product lines of Big Data stream processing applications introduces a number of novel challenges to variability modeling. In this paper, we discuss these challenges and demonstrate how advanced variability modeling capabilities can be used to directly model the topology of processing pipelines as well as their variability. We also show how such processing pipelines can be modeled, configured and validated using the Integrated Variability Modeling Language (IVML).

No abstract available.

Variability-rich Software Ecosystems need configuration capabilities just as in any product line. However, various additional capabilities are required, taking into account the software ecosystem characteristics. In order to address these specific needs, we developed the Integrated Variability Modeling Language (IVML) for describing configurations of variability-rich software ecosystems. IVML is a variability modeling and configuration language along with accompanying reasoning facilities.

The EASy-Producer product line environment is a novel open-source tool that supports the lightweight engineering of software product lines and variability-rich software ecosystems. It has been applied in several industrial case studies, showing its practical applicability both from a stability and a capability point of view. The tool set integrates both, interactive configuration capabilities and a DSL-based approach to variability modeling, configuration definition and product derivation. The goal of the tutorial is to provide the participants with an overview of the tool. However, the main focus will be on a brief introduction of the DSLs. After participating in the tutorial, the participants will understand the capabilities of the toolset and will have a basic practical understanding of how to use it to define software ecosystems and derive products from them.

Industrial variability models tend to grow in size and complexity due to ever-increasing functionality and complexity of software systems. Some authors report on variability models specifying several thousands of variabilities. However, traditional variability modeling approaches do not seem to scale adequately to cope with size and complexity of such models. Recently, textual variability modeling languages have been advocated as one scalable solution.

In this paper, we describe EASy-producer, a prototypical production environment for software product lines (SPL), in particular for the realization of adaptive systems and dynamic SPL.

Abstract Code transformation and analysis tools provide support for software engineering tasks such as style checking, testing, calculating software metrics as well as reverse‐ and re‐engineering. In this paper we describe the architecture and the applications of JTransform, a general Java source code processing and transformation framework. It consists of a Java parser generating a configurable parse tree and various visitors (transformers, tree evaluators) which produce different kinds of outputs. While our framework is written in Java, the paper further opens an opportunity for a new generation of XML‐based source code tools. Copyright © 2004 John Wiley & Sons, Ltd.