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.

Abstract Decision-making systems are prone to discrimination against individuals with regard to protected characteristics such as gender and ethnicity. Detecting and explaining the discriminatory behavior of implemented software is difficult. To avoid the possibility of discrimination from the onset of software development, we propose a model-based methodology called MBFair that allows for verifying UML-based software designs with regard to individual fairness. The verification in MBFair is performed by generating temporal logic clauses, whose verification results enable reporting on the individual fairness of the targeted software. We study the applicability of MBFair using three case studies in real-world settings including a bank services system, a delivery system, and a loan system. We empirically evaluate the necessity of MBFair in a user study and compare it against a baseline scenario in which no modeling and tool support is offered. Our empirical evaluation indicates that analyzing the UML models manually produces unreliable results with a high chance of 46% that analysts overlook true-positive discrimination. We conclude that analysts require support for fairness-related analysis, such as our MBFair methodology.

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Abstract Various semi-formal syntax templates for natural language requirements foster to reduce ambiguity while preserving human readability. Existing studies on their effectiveness focus on individual notations only and do not allow to systematically investigate quality benefits. We strive for a comparative benchmark and evaluation of template systems to assist practitioners in selecting appropriate ones and enable researchers to work on pinpoint improvements and domain-specific adaptions. We conduct comparative experiments with five popular template systems—EARS, Adv-EARS, Boilerplates, MASTeR , and SPIDER. First, we compare a control group of free-text requirements and treatment groups of their variants following the different templates. Second, we compare MASTeR and EARS in user experiments for reading and writing. Third, we analyse all five meta-models’ formality and ontological expressiveness based on the Bunge-Wand-Weber reference ontology. The comparison of the requirement phrasings across seven relevant quality characteristics and a dataset of 1764 requirements indicates that, except SPIDER, all template systems have positive effects on all characteristics. In a user experiment with 43 participants, mostly students, we learned that templates are a method that requires substantial prior training and that profound domain knowledge and experience is necessary to understand and write requirements in general. The evaluation of templates systems’ meta-models suggests different levels of formality, modularity, and expressiveness. MASTeR and Boilerplates provide high numbers of variants to express requirements and achieve the best results with respect to completeness. Templates can generally improve various quality factors compared to free text. Although MASTeR leads the field, there is no conclusive favourite choice, as most effect sizes are relatively similar.

We present CARiSMA, a tool that is originally designed to support model-based security analysis of IT systems. In our recent work, we added several new functionalities to CARiSMA to support the privacy of personal data. Moreover, we introduced a mechanism to assist the system designers to perform a CARiSMA analysis by automatically initializing an appropriate CARiSMA analysis concerning security and privacy requirements. The motivation for our work is Article 25 of Regulation (EU) 2016/679, which requires appropriate technical and organizational controls must be implemented for ensuring that, by default, the processing of personal data complies with the principles on processing of personal data. This implies that initially IT systems must be analyzed to verify if such principles are respected. System models allow the system developers to handle the complexity of systems and to focus on key aspects such as privacy and security. CARiSMA is available at http://carisma.umlsec.de and our screen cast at https://youtu.be/b5zeHig3ARw.

Security and privacy are increasing concerns for both IT service customers and providers. According to cloud security alliance (CSA), privacy level agreements (PLAs) are intended to be used as appendixes to service level agreements and are likely to become as an industry standardized way for cloud service providers to describe the level of privacy and data protection. In this paper, we introduce an approach to verify whether the system design of a service provider supports the service customer's privacy and security preferences, by exploiting PLAs. In the first step, we formalize the PLAs. To this end, a metamodel for the PLAs is provided. This metamodel is based on the PLA outline provided by CSA, which is originally based on Directive 95/46/EC. In our research, we first investigate if an adaptation of the PLA outline with respect to the Regulation 2016/679 (repealing of Directive 95/46/EC) on the protection of natural persons with respect to the processing of personal data, is required. Afterwards, we describe how the PLAs are used to support model-based privacy and security analyses. Moreover, we explain how the analyses results can be used to refine PLAs. Our approach is supported by the CARiSMA tool. To evaluate the approach, we applied it to a real industry case study.