This example shows you how a designer would get started with bat365 model verification and validation tools. It walks you through how to manage and view requirements in Simulink, run checks for compliance to modeling standards, and check for run-time errors. The example uses Requirements Toolbox, Simulink Check, and Simulink Design Verifier.
Requirements Toolbox lets you author, import, and validate requirements within MATLAB and Simulink, track their implementation and verification status, and quickly respond to requirements changes.
If you have Simulink Test and Requirements Toolbox, you can link your requirements to test cases in the Test Manager. Linking to tests lets you see how requirements are confirmed with tests.
Author temporal assessments for tests with Simulink Test to verify complex timing-dependent requirements. You can use a natural language format with unambiguous semantics to specify the assessments.
Using a triplex selection algorithm as an example, this article shows how model test coverage can be used to identify missing requirements during requirements-based testing.
If you have Embedded Coder and Simulink Coverage, you can analyze coverage for generated code during a software-in-the-loop (SIL) or processor-in-the-loop (PIL) simulation.
Debug complex designs faster by highlighting functional dependencies and producing a simplified model with the Model Slicer feature in Simulink Design Verifier.
This article describes a verification workflow based on Simulink. Topics covered include creating test cases, generating test cases for missing model coverage, and measuring code coverage.
During model development, check and analyze your model to increase confidence in its quality. Check your model against standards such as MAB style guidelines and high-integrity system design guidelines such as DO-178 and ISO 26262.
Learn about Model Quality Objectives (MQO), which have been defined by leaders from the automotive industry and bat365. This standard provides quality objectives for Simulink models at different phases of the software development lifecycle.
This example shows how to detect nonfinite, NaN, and subnormal floating-point values in the sldvexFloatingPointErrorChecks example model. The model consists of floating-point arithmetic operations that result in an error. Perform design error detection analysis to detect these errors in the model.
This example shows how to refine the model for dead logic. The sldvSlicerdemo_dead_logic model consists of dead logic paths that you refine for dependency analysis.