Hence, it is possible to refute such properties by searching the state space to find a state in which the given property is violated. In large and complex systems, generating all states causes the state space explosion problem. However, employing this technique for verification of properties such as the safety and liveness requires that all possible states of a system are generated and then the given property is checked. Model checking is one of the successful techniques in automated verification of software and hardware systems. We illustrate our approach in a complex design-space exploration case study of collaborating satellites introduced by researchers at NASA Jet Propulsion Lab. We show that the proposed approach scales significantly better than existing SAT-solver techniques or the original graph solver without multiplicity reasoning. Based on the refinement of 3-valued scoped partial models, we propose an efficient model generation algorithm that generates models that are both well-formed and satisfy the scope requirements. As a result, well-formedness constraints and multiplicity requirements can be evaluated in an approximated way on incomplete (unfinished) models by using advanced graph query engines with numerical solvers (e.g. In this paper, we propose a 3-valued scoped partial modeling formalism, which innovatively extends partial graph models with predicate abstraction and counter abstraction. Type scopes allow to precisely define the required number of newly generated elements, thus one can avoid the generation of unrealistic and highly symmetric models having only a single type of elements. In many model generation scenarios, one needs more refined control over the generated unit tests to focus on the more relevant models. In order to test those tools, the automated generation of well-formed (or intentionally malformed) graph models is necessitated which is often carried out by solver-based model generation techniques. Experimental results show that our method can automatically generate a system that fulfills all given requirements within a reasonable computation time.Īdvanced tools used in model-based systems engineering (MBSE) frequently represent their models as graphs. Our method can generate a system that meets both functional and quantitative service requirements by combining a search-based design method with constraint checking. In this work, we propose a new intent-based system design method based on search-based design that augments search states with quantitative constraints. To deal with practical cases, intent-based system design engines need to be able to handle quantitative parameters and constraints. However, it has difficulty dealing with constraints on the quantitative parameters of systems, e.g., disk volume, RAM size, and QoS. One such method is search-based system design, which can flexibly generate systems of various architectures. ![]() These methods receive service-level requirements and generate service configurations to fulfill the given requirements. Intent-based system design methods have been developed to help with such tasks. This can be a heavy and error-prone task. Network service providers need to appropriately design systems and carefully configuring the settings and parameters to ensure that the systems keep running consistently and deliver the desired services.
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