Michael Goedicke

Research software engineering research aims at understanding and improving how software is developed for research.

Simulink is extensively used across various industries to model and simulate cyber-physical systems. Most industry-built models contain sensitive intellectual property, which prevents companies from sharing models with interested third parties, such as researchers. Initiatives to replace industry-built models with open-source alternatives exist, however they offer only limited remedy. In this work, we offer a novel approach: a Simulink obfuscation tool named SMOKE, designed to selectively protect intellectual property in models. This allows companies to share relevant parts of their models with researchers or other third parties, while safeguarding all sensitive information. We evaluated the tool on an extensive set of open-source models and found it successfully removes sensitive components, while preserving model structure. A video demonstration of SMOKE is available online at https://youtu.be/2KI6HGHrJ20.

The interdisciplinary design of intralogistics systems (ILS) involves engineers from various disciplines, resulting in the generation of discipline-specific model files with overlapping information. For instance, a conveyor system can be represented from various perspectives, such as 3D-CAD models that capture its geometric information and discrete-event simulation models that depict the system’s dynamic material flow performance. The growing demands for flexible reconfigurability and adaptability in intralogistics systems necessitate frequent updates to engineering models. However, these updates often result in potential model inconsistencies due to insufficient stakeholder communication. Detecting the impact of model changes and related inconsistencies is challenging in practice due to data heterogeneity and complex inter-model relations. To address these challenges, we propose an ontology-versioning approach that automates the identification of inconsistencies resulting from model changes. Our approach facilitates the integration of heterogeneous model data, enables database versioning, detects inconsistencies caused by model updates, and provides traceability for identified issues. The concept is evaluated utilizing models from a prototypical implementation on a lab-sized demonstrator. Note to Practitioners —In the industry, the current development of intralogistics systems often lacks automated synchronization of overlapping model information and consistent model interfaces, frequently leading to contradictions among the models. This has been identified as a significant source of errors in the design of both industrial and academic intralogistics systems, as revealed by a study involving intralogistics experts from different technical disciplines. Effectively managing model inconsistencies is crucial for project success, particularly when frequent model changes occur. A promising approach to tackle this issue is to systematically link model data from different disciplines, through which model inconsistencies caused by inadequate communication among engineers can be identified and prevented. However, in many cases, changes in different model versions and their resulting inconsistencies are not adequately considered. To address this issue, we propose a concept based on ontology versioning that allows for the generation, comparison, and analysis of different versions of an ontological model database. This concept automatically identifies model changes, assesses their impacts on other models, and provides information to assist engineers in problem-solving. The effectiveness of our approach is assessed through an evaluation of three representative change scenarios, simplified from real-world use cases. In future research, we plan to extend the approach to general production systems and incorporate industrial-scale models from the broad range of disciplines involved in the design process.

NFDIxCS is a consortium within the family of NFDI, which defines and establishes a research data management (RDM) infrastructure for Computer Science (CS). Based on a broad community process, the various types of research data, their metadata and quality criteria are agreed upon in the community. The resulting research data, along with all associated supplementary information and context such as software, metadata, and the corresponding execution environment are provided as an integral part of the overall infrastructure to meet the FAIR principles. This extended abstract addresses the interaction of the presented task area Architectures & Interfaces within the NFDIxCS consortium and the planned activities, challenges, and objectives of the recently launched project.

During the development of intralogistics systems (ILS), heterogeneous models are created, which represent discipline-specific views, e.g., control software developed by automation engineers or discrete-event simulation models created by simulation engineers. These models represent discipline-specific views on the system but contain overlapping information. Thereby, keeping the information in different development models consistent is challenging and currently requires high manual effort, which highly depends on the developers’ experience. To overcome this challenge, an approach to link heterogeneous model data and identify potential information inconsistencies within and between models automatically is proposed. The concept is evaluated with a use case containing three typical inconsistencies from five representative engineering models applied in ILS development.