Verification and validation techniques applied throughout the development process enable you to find errors before they can derail your project. Most system design errors are introduced in the original specification, but aren't found until the test phase. When engineering teams use models to perform virtual testing early in the project, they eliminate problems and reduce development time by as much as 50%.
Activities for verification, validation, and test with Model-Based Design can be applied at every stage of the development process.
Uncover incorrect requirements and design flaws early by simulating system behavior to validate requirements and by specifying system design properties that formalize functional behavior.
Gain further insight into system behavior through simulation by creating a system model that includes the software model and the physical and environmental aspects of your system. Associate this system model with system requirements to analyze and validate requirements early in the development process. Physical modeling products such as Simscape let you build functional plant models that can be simulated together with your software model.
MATLAB, Simulink, and SystemTest let you define key simulation scenarios and document and systematically analyze behavior captured in your system model. Analysis results provide early feedback on the completeness and integrity of requirements and can be used to define expected behavior of the software model for further design refinement.
You explore the entire parameter space in simulation to help select those tests that are critical to run on real-time targets or real-world hardware. Running Monte Carlo simulations using SystemTest exercises a model over large, randomized parameter spaces. With Simulink Verification and Validation, you can link your system model to requirements for early insight and validation. Requirements traceability helps manage change and reduce waste in the design lifecycle.
MATLAB and Simulink products let you capture design properties and functional requirements in the modeling environment. You model design properties and analyze models using formal methods to improve your designs and reveal unanticipated functionality that would be difficult to uncover by simulation alone. With Simulink Design Verifier you generate tests for your models and prove model properties.
Model-Based Design incorporates simulation and testing to streamline the design verification process.
Using MATLAB and Simulink products, you refine your design through rapid iterations and verification cycles in an interactive test environment. Early in the development process, you can:
In Model-Based Design you create a system model that serves as an executable specification for component design and verification. Previously developed system-level tests can be reused to verify detailed component designs against the executable specification in simulation. You can also apply model coverage analysis tools from Simulink Verification and Validation to pinpoint untested elements of your design.
Well-maintained, consistent documentation helps you hold more effective design reviews and share designs among peers. MATLAB and Simulink products automate the creation of design documentation, test-result reports, and traceability reports, and let you build customized reports.
Apply modeling standards to your design verification process to reduce the number of errors introduced early in the development process. You can create custom model checks or use modeling checks in Simulink Verification and Validation that help you comply with industry standards, such as DO-178B, IEC 61508, and ISO 26262.
MATLAB and Simulink products support the two standard approaches for verifying embedded software: model-to-software verification and run-time error detection in the source code. With model-to-software verification, a fully verified model of your embedded software serves as a golden reference for comparing its behavior with your handwritten or model-generated software. With run-time error detection, products for run-time analysis apply formal methods on handwritten or automatically generated source code to verify that the code does not have run-time errors. These verification processes are especially important for high-integrity embedded systems.
With Model-Based Design, you develop a model of your embedded software using Simulink. After verifying the system model against requirements or expected behavior, you can generate code automatically from the model to reduce the chance of errors introduced through hand coding.
Model-to-software verification techniques like software-in-the-loop (SIL) testing and processor-in-the-loop (PIL) testing can be applied to handwritten or model-generated code to confirm that the behavior of the software matches the behavior of the model. Formal analysis methods available in Simulink Design Verifier automate the generation of SIL and PIL tests from your model, and Embedded Coder helps streamline the PIL test process.
With Polyspace® code verification products, detect run-time errors and prove the absence of specific errors in C/C++ and Ada source code, whether handwritten, model-generated, or a combination of the two. Mathematical analysis techniques in Polyspace products prove the absence of overflow, divide-by-zero, out-of-bounds array access, and other run-time errors in source code, without requiring program execution, code instrumentation, or test cases.
Design embedded software for high-integrity systems that meet industry standards with Simulink and related tools. DO Qualification Kit provides documentation, test cases, and procedures that let you qualify Simulink or Polyspace software verification tools for projects based on the DO-178 standard. IEC Certification Kit includes certificates and reports from certification authority TÜV SÜD that are based on documented, application-specific verification workflows to help you use Embedded Coder or Polyspace code verification products for projects based on the IEC 61508 and ISO 26262 standards.
Model-Based Approach for ERTMS Railway Wayside System Specification, Validation, and Proof 23:49
This presentation discusses the implementation of Model-Based Design for developing a complex system of wayside and train-side control systems, interlocking, track elements, and supervision level.
A system-level model of your hardware lets you verify the correct behavior of your design against this golden reference. You can optimize design tradeoffs for your digital, analog, or mixed-signal design; verify the impact on system behavior; and reuse the model to verify HDL and circuit implementation.
Verify that your algorithm and component designs meet system requirements by using MATLAB and Simulink models to specify behavior, evaluate alternative designs, and integrate components. You can refine designs to create bit-true and timing-accurate reference models for digital and analog hardware components as well as a reusable test bench.
MATLAB and Simulink products help ensure that your HDL implementations of FPGA and ASIC components match the system-level behavior of your model without creating additional test benches. You verify your implementations by cosimulating MATLAB or Simulink models with HDL simulators from Cadence, Mentor Graphics, and Synopsys using HDL Verifier. You can also automatically generate HDL code and test benches for testing hardware designs in an HDL environment and for prototyping on FPGA development boards using HDL Coder and Filter Design HDL Coder™.
Use Simulink and related products to refine and verify behavioral models of analog and mixed-signal components. You can create designs to handle nonlinearities, timing jitter, and other impairments associated with analog hardware. By cosimulating your models with analog simulation tools from Cadence and Mentor Graphics, you can verify that the circuit implementation matches the golden reference.
Hardware-in-the-loop (HIL) simulation minimizes costs and risks in embedded system development by enabling you to test your embedded system before deploying it in a production environment. You test your embedded system in real time by connecting it to a simulation of the remaining design. This approach enables you to use the same system-level models throughout the development process, from design to real-time HIL simulation.
HIL simulation begins with a system-level model that includes your embedded system algorithm and its operating environment. You can automatically generate C code and HDL code from the plant and environment models to run on a real-time simulator that delivers inputs and receives outputs from the embedded system as the real system would. As a result, you obtain greater value from the system-level model for testing and verifying the real-time performance of your embedded system.
HIL simulation is especially valuable when:
FPGA HIL simulation enables you to verify FPGA implementations of algorithms by leveraging a Simulink system model as a virtual test harness. Blocks in your Simulink system model define connections to an FPGA development board running handwritten or automatically generated HDL code. During simulation, signals from the system model are automatically routed to the FPGA, where the algorithm runs, and the signals from the FPGA return to the next Simulink block. Products from Altera, Xilinx, and MathWorks automatically generate the interconnectivity for FPGA HIL simulation.