Test design with model-based testing
Explore model-based testing: uncover practical challenges vs. theory, optimize your software testing efficiency, and drive automation success.
Model-based testing sounds like the perfect solution - but in practice it often fails due to the gap between test design and automation. The reason: abstract models are difficult to translate into concrete, maintainable tests without exploding complexity or turning software testers into developers. A two-phase approach promises a remedy by specifically separating manual and automated test phases and closing the implementation gap with intelligent tool support. This makes model-based testing not only usable earlier, but also maintainable - without capture-replay nightmares.
Podcast Episode: Test design with model-based testing
In this episode, I talk to Matthias Hamburg about model-based testing. Matthias is an experienced expert in the software testing community and provides valuable insights into the problems and solutions of model-based testing. He reports on studies that show that test automation often does not deliver the desired results and explains the difficulties he has experienced in practice, such as insufficient modeling knowledge and gaps between modeling and test execution. Matthias introduces a new tool designed to close these gaps and emphasizes the importance of no-code generation to make testing easier. A fascinating conversation about the future of software testing!
“The World Quality Report shows that test automation often fails to achieve the business goals that were hoped for.” - Matthias Hamburg
Matthias Hamburg was Managing Consultant at Sogeti Deutschland GmbH until his retirement in 2019. His professional focus is on test analysis, test management and test process improvement. He continues to volunteer for the German Testing Board (GTB) and its umbrella organization ISTQB. Among other things, he publishes the Advanced Test Analyst syllabus and the standard glossary of testing terms in English and German.
Highlights der Episode
- Model-based testing often fails due to the gap between high-level test cases and concrete execution.
- Two-phase approach: First generate abstract high-level test cases, then concretize them with real software - without programming.
- According to the World Quality Report, test automation has not achieved business goals - neither CICD nor efficiency.
- Testers do not need formal modeling training - tools must be accessible to professionals.
- Systematic testing design procedures are lacking in practice - many only test according to gut feeling.
Model-based testing: Why theory and practice often drift apart
The gap between aspiration and reality
Model-based testing (MBT) promises efficiency and quality assurance at the highest level. In practice, however, MBT often fails to deliver the expected results. Studies such as the World Quality Report by Capgemini and Sogeti confirm this phenomenon: test automation often fails to achieve business objectives. CI/CD processes stutter instead of running smoothly, and the expected increase in efficiency does not materialize. There seems to be a gap between the brilliant theory and the sobering practice.
Obstacles on the way to successful implementation
One of the main problems with model-based testing is the lack of professional training in modeling techniques. Imagine someone trying to play a complex piece of music without any knowledge of sheet music - the result will hardly be convincing. Similarly, a lack of training for testers leads to inefficient models. In addition, there is often no seamless transition from modeling to test execution. Gaps open up between test design and implementation, making the process more difficult. Tools such as Selenium or Playwright are helpful instruments, but they do not completely close these gaps.
A ray of hope: An innovative approach in MBT
But there is hope on the horizon. A new tool is entering the scene and addressing precisely these challenges. It initially generates abstract test cases from the model that can be checked manually - a kind of dress rehearsal before the curtain falls. As soon as a concrete application is available, the abstract test cases are executed automatically. This seamless transition from modeling to test execution is like a well-oiled gear in the gears of quality assurance. It is particularly noteworthy that the tool also reacts flexibly to changes in the software and adapts accordingly.
Practical example: Optimization using the two-phase approach
This tool has already demonstrated its strengths in a pilot application. It made it possible to try out new methods and optimize the entire test process. The two-phase approach - first generating abstract test cases and checking them manually, then automating them - has proven to be particularly effective. It not only helps to identify early errors, but also improves overall quality assurance. A win-win situation, you could say.
Looking ahead: potential and opportunities
Of course, this new tool is not the end of the line. There is still room for improvement. Support for data-driven testing could be expanded and additional test design methods such as equivalence class formation could be integrated. But the direction is right. If such tools are used more widely in the future, systematic test procedures could finally be given the status they deserve.
Frequently Asked Questions
Model-based testing improves the efficiency of test automation by creating clear, visual models of software requirements. These models enable structured test case generation, avoiding redundant tests and increasing test coverage. In addition, changes in the software design can be quickly integrated into the test models, which simplifies the adaptation of test automation. This makes the test process faster, more cost-efficient and more precise, as errors are detected at an early stage.
Yes, model-based testing is a suitable method for agile software development. It enables rapid adaptation to changing requirements, as tests can be derived directly from models. The visual representation of functions and processes facilitates communication within the team and promotes understanding of the system. In addition, model-based testing supports the automation of test cases, which increases efficiency and test coverage. It therefore fits in well with the dynamic and iterative nature of agile projects.
Model-based testing increases test coverage by systematically capturing all possible inputs and states of a system in a model. This allows test cases to be generated automatically that cover all relevant functions and exceptions. This reduces the risk of overlooking important scenarios. By using graphical models, it is easier to visualize complex processes and identify gaps in test coverage. Overall, model-based testing leads to a more comprehensive and targeted testing strategy that improves software quality.
Model-based testing can be hindered by unclear models that are difficult to understand and insufficient coverage of test cases. In addition, adapting models to changes in the software can be time-consuming. The team often lacks the necessary expertise to create and use models effectively. Finally, technical difficulties, such as integration into existing test environments, can present additional challenges. These factors have a negative impact on the efficiency and quality of the test process.
Various tools are suitable for model-based testing, including 1. UML-based tools: such as Enterprise Architect or Visual Paradigm. 2. test management tools: such as qTest or TestRail with model support. 3. automation tools: such as SpecFlow or Cucumber, which promote BDD (Behavior-Driven Development). 4. specialized tools: such as ModelJUnit or Ptolemy II for model creation and verification. This software helps to automatically derive test cases from models and increase efficiency in the testing process.
In order to successfully integrate model-based testing into existing test processes, clear test objectives should first be defined. It is then important to create a suitable model that maps the software requirements. These models must then be translated into test cases. Training for the team is essential to ensure that everyone understands the new methods. Finally, suitable tools to support model-based testing should be selected and implemented. Continuous feedback helps to continuously improve and adapt the process.
State models, activity diagrams and decision models are often used in model-based testing. These models make it possible to clearly represent and understand the system behavior. The advantages lie in automated test generation, early error detection and improved test coverage. This makes the test process more efficient and effective, as relevant test cases can be created in a targeted manner.
The main difference between model-based testing and traditional testing methods lies in the approach. Model-based testing uses formal models to automatically generate test cases, which enables more comprehensive test coverage. Traditional methods, on the other hand, are often based on manual scripts and the experience of testers, which can lead to gaps and repetition. In addition, model-based testing is more flexible and quicker to adapt to changes in the system, while traditional methods are usually more time-consuming.
The most important advantage of model-based testing is the automated generation of test cases from models, which reduces time and effort. It also improves test coverage as it precisely maps system behavior. Model-based testing enables early error detection and promotes collaboration between developers and testers by creating a common basis. This increases the quality of the software and makes risks more transparent. Ultimately, this leads to more efficient and targeted test processes.
Model-based testing is a test methodology in which models of the system to be tested are created in order to automatically generate test cases. These models represent the behavior and requirements of the system. The advantages of model-based testing include higher test coverage, faster test case generation and early error detection. It also enables efficient reusability of test cases and reduces the manual effort involved in test execution.
Related Posts
Richard Seidl
•May 19, 2026
Why agentic engineering changes everything
Richard Seidl
•May 12, 2026