Workflow patterns follow the concept of economized development. We rely on patterns of workflow to reduce modeling work, simplify maintenance, and create a record of a project's provenance.
The main workflow to access statically defined context models for testbench execution is called OSCAR.
OSCAR is a knowledge-based system providing semantic web discovery capability according to the available context library ontology and instance data. OSCAR stands for Ontological System for Context Artifacts and Resources. The goal is to provide guide discovery for users to find context models and associated metadata to enable their simulation. The context models include collections of power spectral density profiles (PSDs) and other data sets, which provide the generators for model execution.
The other workflows available are intended to provide dynamic content for probabilistic evaluation, such as what is needed to perform PCC (probabilistic certificate of correctness) and for support of formal verification. In these cases, probability density functions provide the boundaries for and likelihoods for testing extreme values, corner cases, as well as distributions to explore the required state space for text cases.
The workflows can also be used to quickly evaluate model characteristics and collect artifacts for later decision making.