Static vs Dynamic Testing: Understanding the Difference in Model-Based Development

April 14, 2026

In modern software development, ensuring software quality is essential. This is especially true in safety-critical domains such as automotive and embedded systems.

In model-based development, engineers use models to design, simulate, and automatically generate code for complex systems. Because models often serve as the basis for production code, verifying their quality early in the development process is crucial. Late defect detection leads to costly rework and delays.

Two important verification approaches support this goal: dynamic testing and static testing.

Dynamic testing evaluates software or models by executing them and observing their behavior at runtime. Static testing, in contrast, analyzes software artifacts without executing them. As a result, they answer different questions: 'Does it work?' vs 'Is it built correctly?'

Both approaches help teams detect defects at different stages of development. When combined, they create a more effective verification strategy.

Understanding the difference between dynamic and static testing helps development teams apply the right technique at the right time.

What is Dynamic Testing?

Dynamic testing refers to testing activities where the software, code, or model is executed to validate its behavior.
By executing a system with specific inputs, engineers can observe how it behaves under different conditions.

Dynamic testing focuses on problems that appear during runtime, for example:

Incorrect outputs
Logical errors
Unexpected system behavior

Typical forms of dynamic testing include:

Unit testing
Integration testing
System testing
Model simulation

In model-based development environments such as MATLAB and Simulink, dynamic testing often involves simulating models before generating production code. Engineers run simulations to verify that the system reacts correctly to different inputs and scenarios.

Benefits of Dynamic Testing

Dynamic testing helps teams:

Verify real system behavior
Detect runtime errors
Validate functional requirements
Test responses to real input scenarios

Because it evaluates system behavior during execution, dynamic testing is essential for confirming that implemented functionality works as intended.

What is Static Testing?

While dynamic testing requires executing software or models, static testing analyzes artifacts without requiring execution.
In model-based development, static testing often involves static analysis tools. These tools automatically examine models or code to identify potential issues.

Typical examples of static testing include:

Code reviews
Documentation reviews
Static code analysis
Static model analysis

Static analysis is particularly valuable in model-based development, where models define both system behavior and structure.
In environments such as Simulink or TargetLink, static model analysis tools can analyze models automatically and detect issues early in the development process.

For example, MES Model Examiner® (MXAM) performs automated static model analysis for models created in these environments. The tool checks models against modeling guidelines and best practices.

MXAM helps development teams:

Identify guideline violations
Detect structural modeling issues
Improve model maintainability
Support consistent modeling practices

By identifying issues before simulation or code generation, static model analysis helps reduce rework later in the development cycle.

Static vs. Dynamic Testing: Key Differences

Aspect	Dynamic Testing	Static Testing
Aspect: Execution required	Dynamic Testing: Yes	Static Testing: No
Aspect: Focus	Dynamic Testing: Runtime behavior	Static Testing: Code/model structure
Aspect: Typical techniques	Dynamic Testing: Unit tests, system tests, simulations	Static Testing: Reviews, static analysis
Aspect: Goal	Dynamic Testing: Validate system behavior	Static Testing: Detect issues early

Both techniques provide valuable insights during development.

Safety standards such as ISO 26262 recommend a combination of verification techniques. This includes both static analysis and dynamic testing to ensure software quality and functional safety. By combining both approaches, development teams can detect more defects and improve the reliability of safety-critical systems.

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