Model-based testing: Autogenerating comprehensive application tests

Thorough application testing is critical for ensuring code accuracy and fulfilling logical requirements. However, manual test creation is time-consuming, error-prone, and susceptible to human bias. Maintenance becomes a significant challenge, especially with feature additions or logic modifications. Model-based testing offers a solution by automatically generating complete, up-to-date tests based on an abstract application model.

Software development relies on various testing methods, from unit to integration and end-to-end (E2E) tests. While unit and static tests are simpler to write, they don't guarantee seamless interaction between components. Integration and E2E tests, though more time-intensive, provide greater confidence in user-expected application behavior by mimicking real-world scenarios.

The scarcity of integration and E2E tests in applications, despite abundant unit tests, stems from limited resources, time constraints, and insufficient understanding of their importance. Even with existing integration/E2E tests, application changes necessitate extensive rewriting and new test creation, becoming impractical under deadlines.

From Automated to Autogenerated Testing

Current application testing approaches include:

1. Manual testing (no automated tests)
2. Automated testing (scripted tests executed automatically)
3. Test automation (integrating automated tests into the development cycle)

Test automation streamlines test execution, but test creation remains manual. Model-based testing addresses this by allowing developers to describe expected application behavior and automatically generate comprehensive tests, including edge cases.

The process involves:

1. Creating an abstract behavioral model (a directed graph).
2. Generating test paths from the graph.
3. Mapping each path step to executable application tests.

Integration and E2E tests consist of steps alternating between:

1. Verifying application state.
2. Simulating an action (event).
3. Verifying the resulting state.

This mirrors the given-when-then testing style:

1. Given an initial state.
2. When an action occurs.
3. Then a new state is expected.

The model encompasses all possible states and events, automatically generating paths between them, similar to route generation in navigation apps.

Illustrative Example: A Feedback Application

Consider a simple feedback application:

• A panel asks, "How was your experience?"
• Users click "Good" or "Bad."
• "Good" shows a "Thanks" screen.
• "Bad" displays a feedback form.
• Form submission leads to the "Thanks" screen.
• Users close the app via "Close" or the Escape key.

Manual Testing with @testing-library/react

The @testing-library/react library simplifies React app testing. Key methods include:

• getByText: Identifies elements by their text content.
• baseElement: Accesses the root document element for event triggering.
• queryByText: Checks for element existence without throwing errors.

Example tests using Jest and @testing-library/react:

// ... (import statements) ...

describe('feedback app', () => {
  afterEach(cleanup);

  it('should show the thanks screen when "Good" is clicked', () => {
    // ... (test logic) ...
  });

  it('should show the form screen when "Bad" is clicked', () => {
    // ... (test logic) ...
  });
});

These tests, while functional, suffer from repetition and become less maintainable with application changes or edge cases. E2E tests, while more realistic, require separate code and cannot reuse these tests.

State Machine Modeling

The feedback app's behavior can be represented as a finite state machine:

This machine is defined using XState:

import { Machine } from 'xstate';

const feedbackMachine = Machine({
  // ... (state machine definition) ...
});

This model serves solely for testing, independent of the application's implementation details.

Creating a Test Model with @xstate/test

The @xstate/test library helps create a test model from the state machine:

import { createModel } from '@xstate/test';

const feedbackModel = createModel(feedbackMachine);

This model needs state verification tests (using meta.test) and event execution functions (withEvents):

const feedbackMachine = Machine({
  // ...
  states: {
    question: {
      // ...
      meta: {
        test: ({ getByTestId }) => {
          assert.ok(getByTestId('question-screen'));
        }
      }
    },
    // ...
  }
});

const feedbackModel = createModel(feedbackMachine)
  .withEvents({
    CLICK_GOOD: ({ getByText }) => {
      fireEvent.click(getByText('Good'));
    },
    // ...
  });

Generating Test Paths

The model's directed graph allows generating all possible simple paths (no repeated nodes) or shortest paths from the initial state. getSimplePathPlans() generates test plans:

const testPlans = testModel.getSimplePathPlans();

Each plan contains paths to a target state. These paths are then tested:

testPlans.forEach(plan => {
  plan.paths.forEach(path => {
    it(path.description, () => {
      // ... (test execution) ...
    });
  });
});

path.test() verifies states, executes actions, and ensures the final target state is reached. testModel.testCoverage() verifies that all states were tested.

Advantages of Model-Based Testing

Model-based testing simplifies integration and E2E test creation. Maintaining tests becomes easier as only the model needs updating when features change. The abstract model allows using the same model and code for both integration and E2E tests (with minor adjustments for testing environments). It also facilitates exhaustive testing, revealing potential edge cases.

Challenges

Model-based testing requires understanding finite state machines and statecharts. The number of generated test paths can grow exponentially, potentially leading to redundant tests. However, this can be mitigated by using shortest paths or model refactoring.

Conclusion

While requiring initial effort in model creation, model-based testing offers significant long-term benefits in test creation, maintenance, and comprehensive coverage. It empowers developers to write fewer, more effective tests, leading to higher quality applications. The provided resources offer further exploration into this powerful testing paradigm.

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