Model based testing is a testing approach where test cases are derived from a model that represents the expected behavior of a system. Instead of writing individual test cases manually, teams first define models such as state machines, flow diagrams, or decision tables that describe how the application should behave under different conditions.

One of the main advantages of model based testing is improved test coverage. Since tests are generated from models, it becomes easier to identify missing scenarios, edge cases, and unexpected state transitions that are often overlooked in traditional test design. This is especially useful for complex systems with multiple workflows, rules, or integrations.

Model based testing also supports consistency and maintainability. When system behavior changes, updating the model can automatically reflect those changes across the generated test cases. This reduces the long-term effort required to keep tests aligned with evolving requirements.

In practice, model based testing works best when combined with automation and CI pipelines. Models act as a single source of truth for expected behavior, while automated execution ensures continuous validation as the application grows. For teams dealing with complex logic or frequent changes, model based testing can bring structure and clarity to the overall testing strategy.