Knowledge-Centric Systems Engineering is an industrial approach to systems and software engineering that advocates the development and use of knowledge bases that represent system domains. This approach can also exploit artificial intelligence techniques. These means can be used for early V&V (verification and validation) activities, e.g., for system artefact quality analysis and traceability management. This paper presents advances made in these activities with the SES Engineering Studio industrial tool and its underlying methods to meet further early-V& V needs in practice. The methods and the tool have been improved to better address model quality analysis, traceability project management, trace specification, and compliance with standards. For validation, we have initially applied the new features on a medical device. This has also allowed us to study the benefits of the features. The results show that the advances made can lead to wider system artefact analyses, more precise traceability management, better system artefacts, lower V & V effort, and lower issue resolution costs. All in all, the paper presents specific examples of how early- V & V industrial practices and tools can be improved.

In critical domains, assurance corresponds to the set of activities to provide confidence that a system can be deemed dependable, e.g., safe and secure. This essential systems and software engineering process are usually conducted according to standards. For novel applications running at the edge and containing artificial intelligence, and for their corresponding platforms, how to conduct assurance in a systematic way is still undefined. This paper introduces the work in the large-scale REBECCA EU project to contribute to filling this gap. A new assurance framework will be defined, addressing the management of compliance, assurance cases, and assurance evidence. The assurance framework will be based on the results of a systematic study to characterize Edge-AI assurance needs, and four systems will be used to validate the framework via case study research. The assurance framework will provide guidance about what needs to be considered for safety and security assurance of Edge-AI applications, and how.

Society increasingly depends on electronic systems and components (ECS) whose reliability must be ensured throughout their lifecycle. ECS reliability must be addressed since early development stages and the means used to this end must fit other systems engineering practices. Within this context, we present an industrial approach for model-based reliability-oriented system design. It links model-based systems engineering with Arcadia/Capella and knowledge-centric systems engineering with the Systems Engineering Suite. The approach deals with system modelling, ontology development, structured textual requirements specification, traceability management, and model quality analysis, all of them targeted at ECS reliability. We also present the validation steps taken.

Context. Many critical systems must meet safety compliance needs from safety standards. These standards are usually large textual documents whose compliance needs can be hard to understand. As a solution, the use of models has been proposed. Goal. We aim to provide evidence of the extent to which models improve the understanding of safety compliance needs. Method. We designed an experiment and ran a pilot to study the effectiveness, efficiency, and perceived benefits of understanding these needs, with the text of standards and with models in the form of UML object diagrams. Results. The overall results from 15 Bachelor students show that the effectiveness of understanding safety compliance needs increases very little with models (2%), and the efficiency even decreases (24%). Nonetheless, the results improve when the potential complexity in navigating the models is taken into account (15% effectiveness increase). The students find benefits in using the models but most consider that the models are hard to understand. Conclusions. The extent to which models improve the understanding of safety compliance needs seems to be lower than what the research community expects. New studies are necessary to confirm our initial insights.