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Abstract Complexity is a core characteristic of concern for systems engineering practice. Verification, while a pervasive process in the engineering of systems, has been given relatively less research focus in terms of its complexity than system complexity has. We have proposed the Verification Complexity Framework as a formal definition of verification complexity to initiate a dialogue on distinguishing verification complexity from system complexity. The framework is designed to cover both static and dynamic complexity through the time axis and the hierarchical complexity layers, covering from external effects to the verification structures. This framework provides a common vocabulary for verification complexity, where both its definition and measurements can be discussed. In this paper, we showcase the application of VCF to a notional project. 

Abstract Knowledge graphs have recently been introduced to the verification strategy field successfully representing the complexity of verification in real‐life applications. This format provides a scale‐free analysis of verification strategies compared to the more traditional verification artifacts such as requirement traceability matrices and verification matrices. Complexities can be observed visually and numerically both in terms of the problem scope and the entity interdependencies. In this paper, we retrieve verification strategy information patterns representing different aspects of verification. This is achieved by tapping into the network properties of knowledge graphs. They are dissected to detect knowledge patterns emerging from different parts of the verification artifacts. Similarities and differences between the two verification strategies are explained numerically and semantically. Seemingly unrelated requirements and verification activities are connected through indirect connections, and orthogonalities between independent requirements are analyzed. These findings validate the scalability of verification planning and assessment based on knowledge graphs.