What is Model-Based Testing: An Overview

OVERVIEW

Model-based testing, aka MBT, is an efficient and systematic software testing approach leveraging models to represent a system's desired behavior.

Such models can be formal notations or graphical representations specifying the functioning of software applications under various conditions. MBT paves the way toward an automated and structured way to verify the compliance of a system with intended requirements and to generate test cases.

What is Model-Based Testing?

The model-based testing approach usually involves creating models representing the structure, requirements, and a software system’s behavior. These models are a steady foundation for managing, executing, and designing different testing activities.

They also accurately represent the software system you're testing, including functional, behavioral, and data models. Capturing the key aspects and dependencies of any software system is rather easy for testers. That's why it offers one of the most comprehensive views of its behavior and structure.

These models are a great foundation for generating test cases by assisting testers in deriving test scenarios, inputs, and expected outputs. In fact, they also automatically create test cases with the help of techniques such as model coverage, analysis, or model transformation. Consequently, when you create these test cases systematically, they comprehensively cover different system functionalities.

When the system scales, models often need updates. In other words, testers are responsible for maintaining these models to ensure they are consistently accurate and relevant. They can also trace these test cases to specific requirements, which helps determine how the changes impact models.

Why Perform Model-Based Testing?

There are several reasons to consider replacing traditional testing methods with model-based testing:

• Incomplete test coverage: Traditional testing, relying on intuition and experience, can leave gaps in test coverage. Complex systems and edge cases are missed, leading to undetectable errors later. By systematically identifying the system behavior based on a model, MBT helps testers ensure comprehensive coverage, catching issues that might otherwise slip through the cracks.
• Time and effort constraints: Creating and maintaining large manual test cases is time-consuming and error-prone. Testers often struggle to keep up, which affects their ability to deliver a thorough test. MBT automates much of this process, freeing up test data from the prototype and freeing up time and resources for other tasks.
• Accuracy and reusability issues: Manual test cases can be inconsistent and susceptible to human error. This creates challenges in maintaining their accuracy and limits their reusability in different versions or similar systems. MBT addresses this by creating test cases from the previous model, ensuring consistency, accuracy, and easy reuse, saving testers time and effort in the long run.
• Late bug detection: Traditional testing often identifies late bugs in the development cycle and is expensive and time-consuming to fix. Testers face the challenge of finding issues early, but manual methods cannot achieve this effectively. MBT allows testing against a precise model of expected behavior and enables testers to point out bugs early when they are easy and cheap to address.
• Communication and collaboration barriers: Different stakeholders (developers, testers, product owners) may have different knowledge of the system, which hinders communication and collaboration. Testers generally try to make sure everyone is on the same page. The MBT process acts as a shared understanding of system behavior, facilitates team communication and collaboration, and results in more efficient and effective development processes.

Benefits of Model-Based Testing

Model-based testing has significant benefits for quality assurance in general. Let's dive into some of the key advantages.

• Efficiency: Model-based testing leads to automation of different aspects of the testing process, from generating test cases to executing them. This automation greatly reduces any manual efforts needed for testing. It can turn out to be especially beneficial for large-scale and complex systems.
• Consistency: The test cases you generate from models ensure that the coverage of the behavior of a system is consistent. As a result, it reduces the chances of missing out on critical tests scenarios that can quickly happen during manual testing.
• Better test coverage: MBT helps identify boundary conditions, edge cases, and various unusual scenarios that can usually be challenging to pick out via manual testing. It leads to better comprehensiveness in test coverage.
• Automatic generation of test cases: Model-based test tools can automatically generate test cases using models, reducing the manual test case design requirement. This can be extremely valuable when you're dealing with software systems with a high level of complexity.
• Early detection of issues: When you create models of the behavior of a system, it’s easy to identify discrepancies and issues and resolve them earlier in development. As a result, you can save greatly on costs.
• Enhance collaboration: MBT facilitates seamless collaboration between testers, developers, and stakeholders. It offers a common visual representation and language of the system, fostering enhanced understanding and communication.
• Reusability: Once you create a model, you can reuse it for different testing phases, such as system, integration, unit testing, etc., across multiple software projects. The reusability saves valuable human effort and time.
• Documentation: Models often serve as vital documentation for the behavior of a system by offering a clear visual representation of the functioning of a system. This makes it more convenient for stakeholders as they can easily understand and verify requirements.

Example of Model-Based Testing

Let’s consider that you are testing an online shopping application with various features such as browsing products, adding items to a cart, checkout, and payment.

Here's how MBT can be applied:

• Model creation:
• States: Define states like browse product, product details, cart, checkout, payment, order confirmation, etc.
• Transitions: Specify actions that trigger transitions between states, such as add to cart, go to checkout, pay by credit card, etc.
• Conditions: Include conditions for transitions, such as enough items on the cart, valid payment information, etc.
• Test case generation: The MBT automatically generates test cases based on the model, covering various scenarios:
• Browse: Testing product search, filtering, and detailed page navigation.
• Add to cart: Checking for valid and invalid item additions, quantity adjustments, and cart updates.
• Checkout: Simulating different scenarios such as address selection, payment method, and order details confirmation.
• Payment: Testing various payment methods, handling errors like insufficient amounts and successful transactions.
• Manage orders: Exploring order tracking, cancellation, and return options.

Shortcomings of Model-Based Testing

While model-based testing offers numerous advantages, it's not without its shortcomings. Here are some of those:

• Model complexity: Creating and maintaining an accurate and comprehensive model can be time-consuming and challenging, especially for complex systems. Other than that, inaccuracies in the model can lead to misleading test cases and missed defects.
• Initial investment: Implementing MBT often requires an initial investment in tools, training, and model development. So, this can be a barrier for smaller organizations or projects with limited resources.
• Limited scope: MBT primarily focuses on functional testing and might not be as effective for non-functional aspects like performance or usability testing.
• Overreliance on the model: Over-dependence on the model can lead to neglecting other test approaches, potentially missing essential edge cases or real-world user interactions.
• Maintenance effort: As the system under test evolves, the model needs to be updated accordingly. This can require ongoing effort and expertise, adding to the overall cost and complexity.

Now, let’s understand the types of model-based testing.

Types of Model-Based Testing

There are two types of model-based testing: online and offline. Each type has unique use cases and characteristics.

• Online model-based testing: This refers to immediately executing test cases using a live software system, which facilitates real-time feedback and interaction. Such an approach is valuable when you require continuous testing. An example includes embedded systems in industries.

Nevertheless, it can be quite resource-intensive and complex, often requiring specialized techniques and tools for interfacing with a live system.

Characteristics:

• Real-time testing: Online model-based testing immediately executes test cases on a live system.
• Immediate feedback: You can access test results in real time, facilitating quick identification of defects.
• Use cases: Appropriate for continuous testing in embedded systems or similar scenarios.
• Complexity: Might need specialized resources and tools for interfacing with a live system.
• Offline model-based testing: This drives more emphasis on generating pre-executed test cases independent of any live system. You can execute such test cases later in a controlled environment and analyze results after test execution. Offline MBT works well for scenarios that demand an exhaustive level of testing, for instance, in complex systems. It's resource-efficient since it doesn't need real-time interaction with live systems.

Characteristics:

• Pre-execution test case generation: You can generate test cases independently of a live system.
• Batch execution: You can execute test cases in a controlled environment, analyzing results afterward.
• Use cases: Handy for exhaustive testing in systems with a high level of complexity.
• Resource efficiency: Efficient with utilization of resources because of no real-time interaction with a live system.

Model-Based Testing Techniques

Now that we understand model-based testing, its benefits, and its different types, let's move on to various techniques. Usually, there are seven primary ones. Let's take a look.

• Finite State Machine (FSM) testing: FSM testing works for systems with clearly defined states and state transitions like embedded systems. You can generate test cases for validating the correctness of various state transitions along with the behavior of the system in each state. Finite state machines offer clarity in visual representations of the system's behavior to make it easier to design tests.
• Statecharts testing: Statecharts are an extension of finite state machines that allow for highly complex modeling using event-driven behaviors and state hierarchies. They are ideal for intricate state management systems such as industrial machinery and aircraft control systems. Complex systems can operate as per expectations by testing with the help of statecharts.
• Decision table testing: Decision tables model complex logic and decision rules. Testers generate test cases for covering various conditions coupled with actions defined in that decision table. Such an approach makes sure that testers end up testing all possible scenarios.
• Unified Modeling Language (UML) testing: UML diagrams, use case diagrams, sequence, activity, etc., model the system's interactions and behavior. You can generate test cases based on these UML diagrams to validate that the software can adhere to set expectations. UML testing is crucial for systems that have detailed behavioral requirements.
• Markov model-based testing: Markov models test systems with probabilistic behaviors, including networking protocols. Such a method assesses a system’s reliability under different probabilistic scenarios to identify vulnerabilities. It's vital for applications where such probabilistic events can have a huge impact on the behavior of a system.
• Data-flow testing: Data flow models analyze the transformation and flow of data inside a system. Test cases concentrate on verifying the accurate flow of data within the system, such as data storage and processing. It is particularly useful for data processing systems, databases, and other data-intensive applications.
• Scenario-based testing: This generates test cases based on specific user stories or usage scenarios. It ensures that any software can meet real-world user behavior and expectations. Such an approach is crucial for applications where user experience and interaction are critical. Some good examples include web and mobile applications.

Steps to Perform Model-Based Testing

Model-based testing utilizes abstract models for designing and generating test cases. Let's take a look at the seven core steps for the working of MBT.

• Create model: Model creation involves making an abstract model representing how the system is expected to behave under testing. Such a model clearly defines transitions, states, and inputs. It also uses different techniques, including decision tables, state charts, state machines, etc. The modeling process frequently involves collaboration between domain experts, developers, and testers to ensure precision and accuracy.
• Validate model: You must validate the model to accurately reflect expected system behavior. Validation includes reviews and simulations to ensure the correctness of the model. Developers, domain experts, and stakeholders offer feedback for validating the model.
• Generate test case: Once you validate the model, you can automatically generate test cases. You can drive these test cases from transitions, states, and inputs, thus ensuring appropriate coverage for different system behaviors. Generating automated test cases saves a lot of effort and time compared to manual test case design.
• Execute tests: Testers execute generated test cases against the system under test, which involves offering inputs, interacting with software, and observing how a system responds. The system behavior is under close monitoring while test execution.
• Compare results: This involves comparing the observed system behavior to its expected behavior as per how the model defined it. Then, testers compare real outcomes with those predicted. It identifies discrepancies, issues, or any points of failure in the software.
• Report defects: When testers discover defects or discrepancies while testing, the development team receives test reports. One of the most significant advantages of MBT is the early detection of defects, which facilitates timely issue resolution. Highly detailed defect reports encompass information about the issue, what triggered the issue, and what model element was involved.
• Maintain and iterate model: With changes or evolutions in a system, an updated abstract model reflects modifications. You can regenerate test cases based on this updated model, which ensures testing alignment with the present application state. With the iterative nature of model-based tests, you can continuously refine and adapt based on evolving requirements and feedback.

Popular Model-Based Testing Tools

Here are some popular model-based testing tools:

• Spec Explorer: This tool is based on the Unified Modeling Language. It lets you create models of your system's behavior using statecharts and activity diagrams and then generate test cases from these models, which you can execute on your system.
• Parasoft SOAtest: It tests service-oriented architectures (SOAs) and web services. It can generate test cases from WSDL and SOAP specifications and is used to record and replay test scripts.
• IBM Rational Test Workbench: This comprehensive testing platform includes various performance, security, and functional testing tools. It also contains a model-based testing tool called TestComposer, which can generate test cases from UML models.
• UModel: It is a UML modeling tool that can also be used for model-based testing. It can generate test cases from UML models and is used to record and replay test scripts.
• Robot Framework: It is an open-source keyword-driven testing framework that can be used for model-based testing. It can create test cases from various sources, including UML models, natural language specifications, and spreadsheets.

These tools can help you run model-based tests. Model-based tests can be run both manually and automatically. However, model-based test automation is the more feasible approach as it creates a model of the software system under test, which can then be used to generate test cases automatically. This model-based automation approach has numerous benefits compared to the traditional one. In the next section, let’s look at how model-based technique improves test automation.

How Model-Based Testing Improves Test Automation?

Test automation is the process of automating test execution using automated testing tools. Here is the breakdown of how model-based testing improves test automation:

• Highly efficient: Automating test case generation saves significant time and effort compared to manual creation. This allows you to test more thoroughly and frequently, leading to higher-quality software.
• More effective: Models can capture complex system behaviors and relationships, providing more comprehensive and accurate test cases. This helps you uncover hidden bugs and edge cases that might be missed with traditional methods.
• Positive and negative test cases: Models can generate positive and negative test cases, ensuring you test both expected and unexpected scenarios. This helps ensure your system is robust and handles errors gracefully.
• Functional and non-functional test cases: Models can also generate functional tests (verifying system behavior) and non-functional tests (verifying performance, security, etc.). This provides a more holistic view of the quality of your software system.
• Unit and integration test cases: Models have the ability to generate unit tests (testing individual components) and integration tests (testing how components interact). This allows you to test different levels of your system's architecture.

To further enhance model-based test automation, you can leverage the capabilities offered by cloud-based testing platforms to run test cases across different permutations of browsers and operating systems.

AI-powered test orchestration and execution platforms like LambdaTest offer a complete test execution environment that helps developers and testers perform automation testing at scale on an online farm of 3000+ real browsers, devices, and operating systems. With LambdaTest, you can run functional and non-functional tests on a scalable cloud grid that expands as your testing needs grow. Furthermore, parallel testing allows you to achieve faster release cycles.

With its AI-infused test analytics platform, you can get a holistic view of your test runs. You can get various metrics about your test case health, test summary, browser, device, OS categorization, test trends, and more.

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Common Misconceptions About Model-Based Testing

While model-based testing offers advantages, some common misconceptions can lead to unrealistic expectations or impede its adoption. Here are some key points to clarify:

• MBT replaces all other testing methods: MBT excels in functional testing but shouldn't replace other approaches like exploratory, performance, or usability testing. They complement each other for comprehensive coverage.
• MBT is a quick fix for all testing challenges: While MBT automates test case generation, creating and maintaining accurate models requires time and expertise. It might only be suitable for some projects, especially simple ones, due to the initial investment.
• The model guarantees perfect testing coverage: The model's accuracy dictates the test cases. Inaccuracies can lead to missed defects or misleading tests. Regular review and updates are crucial for maintaining model effectiveness.
• MBT is only for complex systems: While MBT shines with intricate systems, it can benefit simpler ones, too. Its ability to automate test cases and ensure consistent coverage can be valuable for various project sizes.
• Anyone can use MBT tools effectively: Understanding modeling techniques, the system under test, and the chosen MBT tool is crucial for success. Training and expertise are necessary to leverage the tool's full potential.
• MBT only tests user interfaces: MBT primarily focuses on functional behavior, not solely on UI aspects. However, some tools can partially test UI elements but not necessarily their usability or user experience.
• MBT eliminates human involvement in testing: While MBT automates test case generation, human expertise remains critical. Testers analyze results, identify issues, and adapt models as needed. Collaboration with developers and other stakeholders is crucial.
• MBT is only suitable for traditional software development: MBT can be adapted to Agile methodologies with incremental model updates and test case generation. It can also be used for testing web applications, embedded systems, and other non-traditional software scenarios.

By understanding these common misconceptions, you can make informed decisions about when and how to leverage MBT effectively within your testing strategy.

Best Practices for Model-Based Testing

By now, we have already established that MBT is one of the most valuable approaches to improve test coverage and efficiency. But, to make the most out of model-based testing, it's important to follow certain best practices. Let's dive into them in detail.

• Define clear objectives: Defining clear objectives is the core pillar of all your testing efforts. Once you know what you wish to achieve with model-based testing, you will likely get enhanced test coverage, better identification of defects, and quicker testing cycles.
• Choose the correct modeling technique: Different modeling techniques are appropriate for different scenarios. Appropriately choose between statecharts, Finite State Machines, and UML diagrams.
• Identify critical scenarios: Drive emphasis to test scenarios based on how critical they are to the system. You should concentrate on those significantly impacting the security, performance, and functionality of the system.
• Involve stakeholders early: It's crucial to collaborate with stakeholders, business analysts, developers, and other important people to ensure the accurate representation of requirements and system behavior by models. When they are involved early on, it prevents ambiguities and misunderstandings.
• Use the right MBT tool: The success of your model-based testing approach entirely relies on the tool you use. Select one in alignment with the requirements of your project and the modeling techniques you want to use.
• Model validation: It's crucial to verify that the models accurately reflect system requirements and behaviors with the help of stakeholder reviews and validations to ensure consistency and completeness.
• Automate test case generation: Automating test case generation is known to ensure consistency and reduce manual effort. Therefore, ensure that the tool you are picking automates the generation of test cases.
• Coverage criteria: Always define coverage criteria, including part coverage, decision coverage, transition coverage, or state coverage. Make sure that all your test cases meet these criteria. It's one of the most crucial factors in helping you achieve more comprehensive testing.
• Realistic test environment and human expertise: The test environment should resemble the production environment as closely as possible. Whether in terms of network conditions, software configurations, or hardware. Despite the increased use of automation, always use human expertise to analyze, review, and interpret different test results. Critical manual inspection can still identify many issues that sometimes automated tools miss out on.
• Monitoring and metrics: Metrics help identify areas that could improve and demonstrate how valuable MBT is to management. Monitoring helps in improving the effectiveness of model-based testing.

Conclusion

All in all, model-based testing is one of the important types of software testing, with the ability to generate test cases using abstract models.

Not only does MBT optimize resource allocation, but it also facilitates efficient regression testing and clear communication. The current day software landscape continuously evolves and MBT plays significant role by standing as an adaptable, cost-effective, and reliable testing methodology.