Model-based testing is an approach in which test cases are automatically generated from models. Models are the expected behavior of the system under test and can be used to represent a testing strategy. They are an abstraction of real-world functions.
QA strategizes what to test. With model-based testing, developers and testers share the responsibility to focus only on the models that need to be created around system requirements and specifications. This enables teams to minimize test suite maintenance and generate more tests using different algorithms.
Model-based testing can be used with popular software testing tools and automation frameworks to help the team create both manual and automated scripts and increase the coverage.
How to Start with Model-Based Testing?
One cannot simply implement model-based testing for the entire process at once. It’s a gradual process that starts with a typical scenario presupposing manual creation of test design, where requirement changes have a significant and often unpredictable impact on tests.
The first step of model-based testing is model creation. Models can be created by testers, developers, or business analysts and can cover any level of requirements, from business logic to user story, and link them to each other.
Test cases can be automatically generated once the models are ready. These tests will also be automatically updated once any changes to the model are introduced. Software testing tools made specifically for model-based testing can help identify updated tests, the new ones, and tests that became obsolete due to model changes.
Model-based testing maximizes coverage with the least number of tests. The manual calculation is less efficient and less accurate than MBT options. Along with automated creation and updating of model-based tests, the MBT approach traces the correlation between tests and requirements.
The next stage is the automation of the entire process of test automation. The tautology is on purpose, allow me to explain. First, you need to connect test automation engines and models to generate test scripts. This will create scripts for automated tests faster and will allow teams to easily maintain these scripts under model changes.
Automated tests can be integrated into CI processes and tools once you’re able to generate these tests from models. By integrating with performance testing, service virtualization, test data management, and more, MBT can achieve its full potential.
Machine learning and data analytics are very helpful in optimizing MBT to a dynamic adaptive framework that will be able to forecast defects, offer quality risk evaluation, predict testing routes, and so on.
Significance
Model-based testing is more part of the software development process than independent script tasks. It eliminates the need for test script development and forecast script maintenance. However, it does not eliminate the maintenance of created models within the code which are part of the software development process. This helps improve the software quality by getting the team to think about the models. With reduced test suite maintenance, it allows for flexibility to generate many tests using different algorithms.
Challenges
Undoubtedly, there are numerous benefits of MBT but to fully reap them, you need to be aware of the challenges. One of which is training software developers and testers on model-based testing. Developers are familiar with coding paradigms, but they also need to have the testing knowledge to develop testable applications. Testers need to learn the concept of modeling over traditional testing methods.
To implement MBT, you need proper infrastructure. Modeling is not easy and therefore you need to be equipped with a capable tool that is scalable, handles complex models, and provides reliable test coverage. The initial implementation of the tool will take time. It will offset the long-term goal of reducing test maintenance and increasing test coverage. It can be a challenge to fine-tune MBT in parallel with the creation of models and can result in tool refactoring.
MBT adoption starts by implementing modeling into the development workflow. It can be challenging to shift the mindset and culture as far as how to develop and test applications. It’s important to modify the frontend application code to improve testability.
Conclusion
MBT reinforces the idea that the involvement of QA belongs at the beginning of the discovery stage. It incorporates testability into the product design when it comes to the creation of models for a new/modified feature. Typically, it finds specification and design bugs before the code even exists. With the automation generation of test suites, testing thoroughness increases, test coverage is guaranteed, and the test suite maintenance is eliminated. It’s part of your reporting results to individuals or the team and continuous testing strategy.
