What Is MBT and Why Is It Important?
Model-based testing (MBT) is a software testing approach where test cases are automatically derived from a model that represents the expected behavior of a system. The model is an abstract, formal description of how the system should behave, often expressed using state machines, state charts, decision tables, flow diagrams, or formal specifications. Instead of manually writing individual test cases, testers define the system’s behavior once in the model, and tests are generated from it.
In MBT, the model typically captures inputs, outputs, states, transitions, and business rules. Test generation tools then explore the model to produce test cases that cover specific criteria, such as all states, all transitions, boundary conditions, or specific usage scenarios. The generated tests can be executed automatically against the system under test, and the results are compared with the expected outcomes defined by the model.
Model-based testing is important for several reasons. First, it improves test coverage. Because tests are systematically generated from a behavioral model, it is easier to achieve thorough and measurable coverage of system states, paths, and edge cases that might be missed with manual testing.
Second, MBT increases efficiency and scalability. Creating and maintaining a single model is often less time-consuming than maintaining large numbers of manual test cases, especially as systems grow in complexity. When requirements change, updating the model and regenerating tests is usually faster and less error-prone than rewriting tests individually.
Third, MBT enhances consistency and traceability. Since tests are derived directly from a formalized representation of requirements or expected behavior, there is a clear link between requirements, models, and tests. This makes it easier to detect ambiguities or inconsistencies in requirements early and to demonstrate coverage for audits or compliance.
Fourth, MBT supports early defect detection. Building the model forces stakeholders to think precisely about system behavior, often revealing missing requirements, logical errors, or conflicting rules before implementation or testing even begins.
Finally, MBT enables higher levels of automation. It fits well with continuous integration and continuous testing practices, where tests need to be generated, updated, and executed frequently and reliably.
What Are Different Ways MBT Is Used?
Model-based testing (MBT) is used in several different ways depending on the goals of the team, the maturity of the process, and the type of system being tested. The most common uses are outlined below.
One way MBT is used is for test case generation. Teams create a behavioral model of the system and automatically generate test cases from it. These tests may target functional correctness, edge cases, or specific coverage goals such as all states, transitions, or decision paths. This is the most common and straightforward use of MBT.
Another way MBT is used is for test optimization and selection. Instead of generating all possible tests, the model is used to select a smaller, high-value subset of tests based on risk, priority, or usage frequency. This is especially useful for regression testing, where running the full test suite may be too costly or time-consuming.
MBT is also used for requirements validation. By modeling expected behavior before or alongside implementation, teams can validate requirements early. Walking through the model with stakeholders helps uncover missing requirements, ambiguities, or logical conflicts, reducing the risk of building the wrong system.
Another use of MBT is test oracle generation. The model defines the expected behavior of the system, so it can act as an oracle that determines whether the system’s actual output is correct for a given input sequence. This is particularly valuable in complex systems where expected outcomes are difficult to define manually.
MBT is often used for simulation and what-if analysis. The model can be executed independently of the real system to explore scenarios such as error conditions, rare events, or extreme sequences of actions that may be hard to reproduce in a real environment.
In safety-critical and regulated domains, MBT is used to support compliance and certification. Models provide a clear, formal link between requirements and tests, helping demonstrate coverage and adherence to standards. Generated tests and coverage reports can be used as evidence during audits.
MBT is also used in continuous testing and DevOps environments. As the system evolves, the model is updated and tests are regenerated automatically, enabling fast feedback and keeping test suites aligned with current behavior.
Additionally, MBT can be used as a communication and documentation tool. The model serves as a precise, visual, or formal representation of system behavior that can be shared across developers, testers, and business stakeholders, improving shared understanding.
In practice, organizations often combine several of these uses, applying MBT not just as a test generation technique but as a broader approach to understanding, validating, and testing system behavior.
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How Is MBT Implemented?
- Define the testing objectives and scope, such as functional coverage, risk-based coverage, or regression testing needs
- Select an appropriate modeling approach, for example state machines, state charts, decision tables, flow models, or formal specifications
- Identify the level of abstraction for the model so it captures essential behavior without unnecessary implementation detail
- Build the behavioral model based on requirements, business rules, and expected system behavior
- Review and validate the model with stakeholders to ensure correctness and completeness
- Choose coverage criteria, such as state coverage, transition coverage, path coverage, or boundary conditions
- Configure test generation rules and constraints to control the number and type of test cases produced
- Automatically generate test cases from the model using MBT tools or frameworks
- Map model elements to the system under test, including inputs, outputs, and interfaces
- Implement adapters or glue code to connect generated tests to the actual system
- Execute generated tests automatically or manually, depending on the environment
- Compare system behavior with model-defined expected results to determine pass or fail outcomes
- Analyze test results and coverage metrics to identify gaps or defects
- Update the model when requirements or system behavior change
- Regenerate tests and repeat execution as part of ongoing development or continuous testing processes
What Are the Key Components of MBT?
- System model
An abstract representation of the system’s expected behavior, including states, transitions, inputs, outputs, and business rules. - Modeling language or notation
The formalism used to describe the model, such as state machines, state charts, decision tables, flow diagrams, or formal specification languages. - Test generation criteria
Rules that define how tests are derived from the model, such as state coverage, transition coverage, path coverage, or risk-based criteria. - Test generator
A tool or engine that explores the model and automatically produces test cases based on the selected coverage criteria. - Test oracle
The mechanism that determines the expected outcome of each test, often derived directly from the model. - Mapping or adapter layer
The connection between abstract model actions and the concrete interfaces of the system under test, translating model inputs and outputs into executable test steps. - Test execution environment
The framework or infrastructure used to run the generated tests against the system, including automation tools, simulators, or real hardware. - Coverage and analysis mechanism
Tools or metrics used to measure how much of the model and system behavior has been exercised and to identify gaps or weaknesses. - Model and test maintenance process
The practices used to update the model and regenerate tests as requirements or system behavior evolve.
What Technologies and Tools Are Used Alongside MBT?
- Modeling languages and notations such as UML, SysML, state machines, state charts, decision tables, and formal specification languages
- MBT tools and frameworks that support model creation, test generation, and execution
- Test automation frameworks used to execute generated tests against the system under test
- Programming languages used to implement models, adapters, and test logic
- Continuous integration and continuous delivery tools to automate test generation and execution in pipelines
- Requirements management tools to link models and tests back to system requirements
- Test management tools for storing, organizing, and reporting on generated test cases and results
- Simulation and virtualization tools to emulate external systems, hardware, or environments
- Mocking and stubbing frameworks to isolate components during testing
- Code coverage and analysis tools used alongside model coverage to assess overall test effectiveness
- Configuration management and version control systems to manage models, tests, and related artifacts
- Reporting and analytics tools to visualize coverage, defects, and test outcomes
What Are Likely Future Uses for MBT?
Likely future uses for model-based testing (MBT) are driven by increasing system complexity, higher automation demands, and tighter integration with modern development practices.
One major future use is deeper integration with continuous delivery and DevOps. MBT models will increasingly serve as living artifacts that automatically regenerate tests whenever requirements, code, or configurations change, enabling continuous validation at scale.
MBT is also likely to be used more extensively with AI- and data-driven systems. Models can define expected behavioral constraints, safety boundaries, and invariants for systems whose internal logic is probabilistic or learned, helping validate correctness even when exact outputs cannot be predicted.
Another future use is in digital twins. MBT models will act as executable twins of real systems, allowing teams to test scenarios, failures, and upgrades in parallel with production systems, especially in domains such as automotive, aerospace, and industrial automation.
Increased use in safety-critical and regulated systems is also likely. As regulations demand stronger evidence of verification and traceability, MBT will be used to automatically produce coverage metrics, test evidence, and compliance artifacts.
MBT is expected to play a larger role in early lifecycle activities. Models will increasingly be used before implementation for requirement exploration, design validation, and risk analysis, shifting testing further left and reducing late-stage defects.
Another likely use is large-scale regression testing optimization. MBT models can help intelligently select or generate only the most relevant tests based on system changes, reducing test execution time while maintaining confidence.
MBT may also be combined more tightly with formal verification and static analysis. Models can serve as a common foundation for multiple verification techniques, improving consistency between design, testing, and analysis.
Also, MBT is likely to become more accessible through low-code and domain-specific tools. This will enable non-test specialists, such as domain experts or business analysts, to contribute directly to model creation and test definition, broadening MBT adoption across organizations.
Is MBT Overseen by Any Key Standards and Guidelines?
Yes. Model-based testing (MBT) is guided and influenced by several well-known standards and guidelines, although it is not governed by a single, MBT-specific standard. Instead, MBT fits within broader software testing, systems engineering, and quality standards.
The ISO/IEC/IEEE 29119 software testing standard family is the most directly relevant. It defines general testing concepts, processes, documentation, and techniques, and explicitly recognizes model-based testing as a valid test design technique. MBT implementations are typically aligned with its guidance on test design, test coverage, and test documentation.
In systems and software engineering, ISO/IEC/IEEE 15288 (systems life cycle processes) and ISO/IEC/IEEE 12207 (software life cycle processes) provide frameworks in which MBT can be applied. These standards support the use of models for specification, verification, and validation across the lifecycle, which naturally includes MBT.
For modeling itself, standards from the Object Management Group are important. UML and SysML define commonly used modeling notations that are frequently used as the basis for MBT models. While these standards do not define testing processes, they standardize how behavior is modeled.
In safety-critical and regulated domains, MBT is often aligned with domain-specific standards. Examples include ISO 26262 for automotive functional safety, IEC 61508 for industrial safety, DO-178C for avionics software, and EN 50128 for railway systems. These standards emphasize traceability, coverage, and evidence-based verification, all of which MBT supports.
In test maturity and process improvement contexts, models and MBT practices are compatible with frameworks such as TMMi and CMMI, which encourage systematic, measurable, and repeatable testing approaches.
In summary, MBT is not controlled by a single governing standard, but it is well supported by international testing, engineering, modeling, and safety standards that provide guidance on how MBT should be applied, documented, and assessed.
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Attendees also:
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- Learn how SysML is used to model verification and validation
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- Create a plan to deploy MBSE and SysML technologies in your organization to support all test and evaluation (T&E) tasks and processes
- Use Model Based Testing (MBT) along with MBSE approach for specifying, developing and testing complex systems
- Create Model Based Testing (MBT) plans with requirements and V&V Domains
- Learn about Model-Based Systems Engineering (MBSE) key testing artifacts
Who Should Attend?
Model Based Testing/MBSE Training Course is designed for test engineers, analysts, project managers and anyone else involved in test and evaluation, systems engineering and acquisition, modeling, simulation, DoE, and O&M.
For more information, contact us.
