Model Based Testing: Benefits, Use Cases & Best Practices

Every digital experience we rely on – from booking cabs to transferring money — runs on dynamic, interconnected software systems. The speed at which applications are evolving is much faster than the traditional test approach can keep up with. Manual scripting breaks whenever there is a change to the user interface; automation will require regular maintenance to fix the automated scripts; and teams are continually losing confidence in the release stability.

Model based testing (MBT) is a new way of looking at how we can automate the creation of tests that adapt with the evolution of products through converting the behaviours of systems into graphical representations or Models. In this blog, we’ll break down what MBT means in software testing, how it works, when to use it, examples, tools, pitfalls, and how it complements modern test automation practices.

What is Model Based Testing?

Model Based Testing (MBT) is a software testing methodology that uses mathematical or visual representations of expected software behavior to create tests for automated test automation. The mathematical and/or visual representation of expected software behavior (the "model") describes:

• What users can do and how the system will respond

• What user actions can (and cannot) be input into the system

• How users can navigate in the system (the navigation flows) and what logical rules govern the users’ ability to navigate through the system

• What is expected to happen as the user transitions from one action to the next

Using the information provided in the model, automated tests can be generated. This provides for faster scaling of test design while keeping the alignment of the test design with the business rules.

Why Should Teams Care about Model Based Testing?

Conventionalized testing can be tedious, slow to create, and do not evolve with user-interface adjustments because it has no association with ‘real requirements’.

Whereas model based software testing provides:

MBT allows a shift away from creating manual scripting to having intelligent test generation build from the actual design of the system.

How Does Model Based Testing Work?

MBT typically follows a 4-step workflow:

1. Model the behavior of the system (state diagrams, flow charts, & Decision graphs)

2. Add the logical definitions for the system (rules, constraints, and transitions between states)

3. Automatically generate Executable tests for the system

4. Execute the tests and see what the test coverage is and if the test has failed.

The MBT model acts as a central source for all validation performed by both Dev and QA, and therefore is the foundation for the overall ‘traceability‘ process through unified communications about both Development environmental and Quality Assurance testing processes.

What are the Core Modeling Concepts in Model Based Testing?

The common modeling techniques are:

• State-based models (i.e. login → dashboard → profile → logout transitions), flow models (user journeys/business process validation)

• Data models (input constraints/transformation checks)

• Decision tables/finite state machines (verification of every rule based outcome).

The base of these models will allow model based testing to be scalable, sustainable, and not obsolete in the future.

How to Generate Executable Tests from Models?

There are several techniques that tools use to convert a model into an actual executable test.

Some examples are:

• Path Coverage Algorithms,

• Constraint Solving

• Equivalence Partitioning

• Risk-Based Selection

Path coverage ensures that testers will only select the paths that have significant risks associated with them; something that minimizes the redundancy of flows and maximizes the efficiency of testing.

Methods for Execution:

1. Offline Execution: An offline approach to generating executable tests will first generate the tests from the model, and then execute each separately against the SUT. This is useful when you want to perform regression tests, or when you have an environment where you want to separate the creation of tests from their execution.

2. Online Execution: Tests are generated and executed at the same time in an online method, allowing rapid verification of the behavior of the system and providing feedback in near real-time to Agile and DevOps pipelines.

This approach can be used for testing AI models, where the model can create intelligent tests and continuously evaluate how the system behaves.

Use of Model Based Testing for APIs and Microservices

Using model-based testing (MBT) for testing APIs and microservices provides significant advantages over traditional approaches to testing in today’s distributed environments, such as:

• Assists with validating the workflow of APIs

• Verifies the dependencies of microservices and testing service contracts.

• Performs reliable regression testing without having to rewrite test scripts.

• API-first architecture complements the rule-based model structures.

When to Use Model Based Testing and When Not to?

Utilize Model Based Testing When:

• Requirements Change Frequently

• Systems Have Many Complex State Transition Paths

• Requirements Require Complete Logical Validation

• Collaboration Between QA & Dev Is Important

Do Not Use Model Based Testing When:

• The Application Is Too Simple To Require Modeling

• Requirements Are Unclear Or Change Too Frequently

• Team Does Not Have Initial Modeling Skill-Set Available

What are Real Model-Based Testing Examples that Teams Can Try?

Here are examples of model based testing that a team could try:

• Multi-factor authentication in the signup/login process.

• Transaction rules for Fintech or Wallet.

• Synchronizing the Shopping cart inventory.

• API Throttling/Rate-Limiting Paths.

• Validating Role Based Authorization.

Introducing Model Based Testing (MBT) can be done by adding one feature at a time and building your model library.

Which Model Based Testing Tools Should Teams Evaluate?

When considering various options from several popular model based testing, you can conside these widely adopted, tried and tested tools:

• GraphWalker (open source — supports FSM and path-based generation)

• Conformiq (enterprise MBT with graphical modeling)

• AccelQ (model-driven testing for enterprise workflows)

• Spec Explorer (Microsoft — strong for .NET model testing)

These tools operate at their most effective when integrated with existing test automation tools and CI workflows.

How to Integrate Model Based Testing into CI/CD Pipelines?

Here are some steps for integrating model based testing into CI/CD pipelines:

• Store and version models in Git

• Auto-generate test cases whenever a pull request is made.

• Run MBT scenarios in CI pipelines

• Track coverage and model change histories

• Update models as the requirements shift.

As a result, software will continue to be tested as fast as it is released.

What are Common Pitfalls in Model Based Testing Adoption?

The most common pitfalls of implementing model-based testing in software testing include the following:

• Creation of very complicated models from the very beginning.

• Modelling too much of irrelevant details.

• Using tools with steep learning curves often makes it difficult for testers who are not familiar with the tools.

• Missing traceability with requirements.

So the key lies in simplicity: Start small → Validate → Iterate

Best Practices for Model Based Testing

The following five foundational best practices will maximize the value of model-based testing for teams.

• Begin by focusing on the most important aspect of the system that can be modelled (i.e. the most critical flows) instead of trying to capture all of the functionality of the application at once.

• Ensure your models stay relevant and easily maintainable by putting them in Version Control and revising them as the requirements change.

• Use automated test generation but make sure to regularly validate that the tests created by the automated testing tool still accurately reflect what a business considers to be accurate results and are representative of user behaviour or application edge-cases.

• Apply CI/CD processes to automatically integrate your models into your CI/CD environment so that you receive feedback sooner on build quality and allow developers to validate new code as it goes through an automated test process.

• Encourage collaboration between QA, Development, and Product Teams to create models that accurately represent how the system is expected to behave.

How does Model Based Testing Complement Keploy?

Model based testing has a different purpose, but one that complements Keploy. MBT uses models to verify an application’s functionality to ensure it meets design and logic expectations, whereas Keploy validates systems based on actual API activity in an environment similar to production.

Together, these solutions provide:

• Intention (the expected behavior) Validation

• Reality (actual behavior from users) Validation

• More reliable and quicker application deployment.

• Higher test coverage and lower manual maintenance.

Keploy strengthens the use of MBT by allowing MBT to ensure the evolving microservice-compatible applications are tested in ways that reflect the real world – not just against idealized models.

Looking Ahead

MBT (Model-Based Testing) introduces a new approach for validating software for high-speed engineering teams. Instead of writing scripts directly (hardcoding), testers create models that will automatically adapt with the changes in business logic. With the movement towards distributed microservices, dynamic processes (e.g., event-driven architectures), and more complex interactions (e.g., IoT), MBT is a viable option way forward for improving quality assurance.

FAQs

What is the Benefit of Using Model-based Testing for User Interface (UI) Testing?

Model-based testing focuses on the user’s behaviour by modelling the user’s actions instead of relying solely on the user’s click-path (which is usually very fragile). By using model-based testing, companies will reduce their reliance on flaky UI automation.

How to Combine Model Based Testing and Risk-based Testing for Effective Test Case Generation?

Risk-based testing will allow testers to create test cases for only the most critical transition points, thereby enabling only the most impactful scenarios to become executable test cases.

Can MBT Generate Maintainable Tests for API-driven Microservices?

Yes – as the model evolves with the service, complete verification of the entire workflow is maintained.

How does Model-based Testing Differ from Script-based or Record-replay Approaches?

Model-based testing is focused on the logic of the behaviour and state transitions of the software. Record-and-replay and script-based testing capture only what a user does (the UI action).

What Modeling Languages are Best for Model Based Testing?

The most commonly used modeling languages in MBT within enterprises are UML, BPMN, DSLs, and statecharts.