Model-Based Testing (MBT) is a software testing approach where test cases are automatically or semi-automatically derived from models that represent the expected behaviour or functionality of the system under test (SUT).
Key Features:
- Model-Driven:
- Test cases are generated based on abstract models, such as state machines, activity diagrams, or decision tables, which define the desired behaviour of the system.
- Automation Focus:
- Emphasises automating test generation, execution and result evaluation, leading to increased efficiency.
- Efficiency and Reusability:
- Models can be reused and updated to adapt to system changes, reducing manual effort.
Advantages:
- Improved Test Coverage: Models help ensure all functional scenarios are covered systematically.
- Consistency: Tests derived from models are uniform and consistent.
- Adaptability: Changes in system requirements can be reflected in the model, leading to automatic updates in test cases.
- Error Detection: Early model validation can uncover inconsistencies or omissions in system design.
Challenges:
- Model Quality: The effectiveness of MBT heavily depends on the accuracy and completeness of the model.
- Learning Curve: Requires specialised knowledge and tools for model creation and maintenance.
- Initial Cost: High initial investment in setting up models and automation frameworks.
Applications:
- Software Development: Testing complex systems with multiple states, such as embedded systems or financial applications.
- Regulated Industries: Ensuring compliance with standards through rigorous and repeatable testing.
- Agile and DevOps: Automating regression tests to accelerate release cycles.