Model-Based Testing in Test Automation

1. Model-Based Testing in Test Automation

2. 1 Table of contents What is Model-Based Testing? Types of Model-Based Testing 1. Decision table testing 2. Finite State Machine (FSM) testing 3. Statecharts testing 4. Data flow testing 5. Control flow testing 6. Scenario-based testing 7. Unified modeling language (UML) testing 8. Markov model-based testing How Does Model-Based Testing Work? 1.Create a model 2.Validate the model 3.Generate a test case 4.Compare the results 5.Compare the defects 6.Maintain and iterate the model

3. 2 Applications of Model-Based Testing Example of Model-Based Testing 1.Write the user story 2.Generate test cases 3.Conduct tests 4.Flag and rectify errors Challenges Related to Model-Based Testing Complement Your MBT Workflows with TestGrid Final Words

4. 3 With market expectations ramping up all the time, software companies everywhere are investing in ways to shorten the development lifecycle. The faster you release new apps, the greater your chances of grabbing market opportunities. But testing a complex product for performance and reliability takes its own time, and rushing things could lead to some errors slipping by unnoticed — even if it’s automation testing. And that’s where model-based testing comes in. This method relies on abstract models to create test cases, rather than human intervention, cutting down the time it takes to put out a top-notch product with near-zero bugs. Let’s take a closer role at the role MBT plays today in ensuring app reliability. What is Model-Based Testing? Model-based testing uses abstract models to write test cases based on the current model of the system being tested. Essentially, these models represent the system’s expected behavior and outcomes in different scenarios. Combined with other frameworks for automated and manual testing, it’s an ideal way to improve test coverage at lower cost, making it perfect for complex systems with multiple possible states. Some model-based tests run entirely on their own, while some might require human inputs at the start. Types of Model-Based Testing 1. Decision table testing For highly complex decision rules, decision tables are a great option. They generate tests that cover all possible combinations of actions and conditions that have been defined. 2. Finite State Machine (FSM) testing FSM testing is ideal when you have well-defined states and transitions. The FSM gives you a visual representation of how the system behaves to simplify test design. Test cases are structured to assess the correctness of system behavior in each state and transition. Embedded systems often make use of FSMs.

5. 4 3. Statecharts testing For even more complex systems with detailed state management and event-driven behaviors, statechart testing is ideal. It essentially expands what FSMs do to validate highly intricate systems, like those used to control industrial equipment. 4. Data flow testing As the name suggests, data flow models study the flow and behavior of data in your system, including data interactions, storage, and retrieval. This makes it ideal for data-intensive applications like databases. 5. Control flow testing This form of testing involves the use of graphs that showcase the control flow of an app, ie, the sequence in which code is executed. It’s a useful option for step-by-step behavioral analysis and more comprehensive coverage. 6. Scenario-based testing For systems where user experience and interactions are paramount, like a customer service app, scenario-based testing is the way to go. It generates test cases based on a variety of real-world scenarios and user expectations to ensure that the software behaves as designed. 7. Unified modeling language (UML) testing This is a valuable form of model-based testing for systems with intricate behavioral requirements. Test cases are designed based on diagrams that include use case, activity, and sequence diagrams to analyze system behavior.

6. 5 8. Markov model-based testing Markov MBTs are used to study system reliability when various probabilistic scenarios take place. It’s ideal for assessing robustness and quickly identifying vulnerabilities for systems like networking protocols. How Does Model-Based Testing Work? Typically, model-based testing involves the following steps: 1. Create a model First, you set up and define an abstract model with the expected behaviors of the system in question. You’re likely to want to work together with testers and domain experts to ensure that you’ve defined all the necessary parameters. 2. Validate the model Then, you conduct reviews and simulations to validate the model so that you know it accurately captures the intended system behaviors. 3. Generate a test case Post-validation, the model automatically starts generating test cases from the different states and transitions. The tests are designed to cover all possible system behaviors. 4. Compare the results Once all the tests are ready, they’re executed against the actual system as and when inputs are provided. This part is typically integrated with your automated testing frameworks.

7. 6 5. Compare the defects Your QA team will be closely monitoring the observed behaviors during test execution to compare them against the expected behaviors. If there are any errors, they need to be flagged immediately. The QA team shares details of all errors or discrepancies with the development team so that they can fix the issue before it compounds. 6. Maintain and iterate the model As you continue working on the system and upgrading it, the abstract model is updated too. With MBT, you can quickly regenerate tests based on this new version of the system model so that you’re always getting the most up-to-date test results. This also lets you embrace continuous improvement by refining your tests based on the feedback at each stage. Applications of Model-Based Testing MBT has a significant advantage over traditional test automation, in that it removes the need to manually draft test case scenarios. This saves your QA team a lot of time, which they can now devote to studying the test results and making sure they haven’t missed any little details. The applications of model-based test automation can be summed up as follows: 1. Comprehensive test case generation that covers different system versions and transitions 2. Streamlined test generation allowing QA team to focus on critical issues like test interpretation and fine-tuning 3. Early detection of any defects, saving on the time and effort devoted to issue resolution

8. 7 4. Inclusion of multiple scenarios, such as edge cases, without manually working on each one 5. Updating the model and test cases as the system evolves for the most relevant results 6. Easy reuse of test cases for regression testing whenever there’s a software update, preserving old functionality when new changes are implemented 7. Easier communication and transparency between testing and development teams 8. Higher quality of the finished deliverable owing to quick feedback and early resolution of issues Example of Model-Based Testing Let’s see how model-based testing works in real life with the help of a simple example — the checkout process in a shopping app. 1. Write the user story The team starts by defining what each step of the checkout process is supposed to look like with the help of a flowchart. 2. Generate test cases The model will generate different test cases based on possible scenarios in the checkout process, such as a successful order being placed, invalid user login, incorrect payment details entered, multi-factor user authentication, or the user changing their mind and going back to the cart at the last step. 3. Conduct tests The tests are conducted to ensure that the app displays the expected behaviors under each of these scenarios without glitching. For instance, if the user login is invalid, the app should display the screen where the user can reenter their details along with an option to check out as a guest instead.

9. 8 4. Flag and rectify errors Any errors are flagged and sent back to the dev team for correction so that the user can enjoy a fast and convenient checkout experience. Challenges Related to Model-Based Testing Like any testing strategy, model-based test automation comes with its share of cautionary notes. Some things to be mindful of include: 1. Choosing the right model that fits into your budget 2. Integrating the model with your existing tech stack and workflow 3. Ensuring that all relevant stakeholders are on board 4. Defining the specifications of the system under testing upfront and in detail 5. Training your testing team to be comfortable with using MBT 6. Ensuring that your model continues to be relevant over time as your software and your industry needs evolve However, all of these can be overcome with prior planning and team-wide communication. Complement Your MBT Workflows with TestGrid One critical USP of TestGrid that you should know about is that it simplifies the process of creating automated tests without any extensive programming knowledge. While our platform doesn’t claim native modeling solutions, it can integrate with external tools. If you have generated test cases through any model-based approaches like state machines or UML diagrams, TestGrid supports integrating them. What’s more, its device and cross-browser testing coverage can allow you to execute MBT test cases on real devices and environments. If your models are aimed at testing system performance, TestGrid’s performance testing tools enable validation of latency, throughput, and stress scenarios produced by MBT.

10. 9 Lastly, its regression testing capabilities ensure that model-based tests stay aligned with evolving requirements. Final Words It’s never a bad idea to assess your software development cycle for opportunities to reduce timelines, and model-based testing is one of the best ways to do so. It helps you find more bugs, across more granular scenarios, earlier on in the testing cycle, saving your team a lot of trouble later on. Have a look at the different MBT frameworks out there to see which one suits your tech stack best. Once you and your team have figured out the ropes, the efficiency gains you’ll see will be second to none. One brilliant option is TestGrid. It’s a robust, AI-powered testing platform — complete with automation and cross-platform support — that bridges the gap between high-level model designs and real-world test execution. For more reference, kindly visit TestGrid.io.