Test-Driven Development with AI Assistance
Learn how to adapt TDD practices for AI-assisted development, ensuring robust code while leveraging AI's strengths.
Test-Driven Development with AI Assistance
Test-Driven Development (TDD) becomes even more powerful when combined with AI assistance. The key is adapting traditional TDD practices to leverage AI's strengths while maintaining the discipline that makes TDD effective.
Why TDD + AI Works So Well
AI excels at generating code to satisfy well-defined specifications, and tests provide exactly that - clear, executable specifications of desired behavior. This combination produces:
- Higher quality implementations that satisfy exact requirements
- Better edge case coverage as AI can generate comprehensive test scenarios
- Cleaner interfaces because tests force you to think about API design first
- Built-in documentation through executable examples
The AI-Enhanced TDD Cycle
1. Write Failing Tests (Human + AI)
Start by writing tests that define the expected behavior, but leverage AI to help with:
Test scenario generation:
I need to implement a user authentication function. Help me brainstorm
all the test scenarios I should cover, including edge cases and error conditions.
Test structure:
Here's the authentication function signature I want to implement:
Write comprehensive tests covering success cases, validation errors,
network failures, and security edge cases.
2. Verify Tests Fail (Critical Step)
Never skip this step. Run the tests and confirm they fail for the right reasons:
npm test -- --testPathPattern=auth.test.ts
This ensures:
- Tests are actually testing something
- Test setup is correct
- You understand what needs to be implemented
3. Commit Failing Tests
This is crucial for AI-assisted development:
git add auth.test.ts
git commit -m "Add failing tests for user authentication
Tests cover:
- Valid login scenarios
- Invalid credentials handling
- Network error handling
- Input validation
- Security edge cases"
Why commit failing tests?
- Creates a clear contract for what needs to be implemented
- Prevents AI from modifying test expectations during implementation
- Provides rollback point if implementation goes wrong
4. Implement to Pass Tests (AI-Driven)
Now leverage AI's strength - generating code to satisfy specifications:
I have failing tests for user authentication in auth.test.ts.
Please implement the authenticateUser function to make all tests pass.
Requirements:
- Don't modify the tests
- Follow our existing error handling patterns
- Use our current HTTP client setup
- Match the exact interface specified in the tests
Critical rule: AI should NOT modify tests during implementation. If tests need changes, stop and reconsider the requirements.
5. Independent Verification
Verify the implementation independently:
Please review the auth implementation against our security checklist:
- Is password handling secure?
- Are error messages appropriate (not leaking info)?
- Is rate limiting considered?
- Are inputs properly validated?
Don't modify the code - just provide a security review.
Advanced Techniques
Property-Based Testing with AI
AI excels at generating property-based tests:
Help me write property-based tests for this sorting function using jest.
Generate tests that verify properties like:
- Output is always sorted
- No elements are lost or added
- Handles edge cases (empty arrays, single elements, duplicates)
Mutation Testing
Use AI to verify test quality:
Look at these tests for the payment processing function. Generate
some mutations of the implementation that should make tests fail.
This will help verify our tests are actually catching bugs.
Test Documentation
AI can generate excellent test documentation:
Create documentation for this test suite that explains:
- What behavior is being tested
- Why each test case matters
- How to add new test cases
- Common failure modes and debugging tips
Common Anti-Patterns to Avoid
Letting AI Modify Tests During Implementation
Wrong:
The implementation isn't passing test X. Should I modify the test
or the implementation?
Right:
The implementation isn't passing test X. Let me review if the test
correctly captures the requirements. If so, fix the implementation only.
Writing Tests and Implementation Simultaneously
Wrong:
Please implement the user service along with comprehensive tests.
Right:
First, help me write failing tests for the user service.
[Commit tests]
Now implement the service to make the tests pass.
Accepting the First Implementation
Wrong:
Accepting the first implementation that passes tests.
Right:
The tests pass, but please review the implementation for:
- Code clarity and maintainability
- Performance implications
- Security considerations
- Alignment with project patterns
Integration with Existing Codebases
Understanding Test Patterns
Before writing new tests, understand existing patterns:
Please analyze our existing test files and document:
- Testing utilities and helpers we use
- Mocking patterns for external dependencies
- Assertion styles and preferred matchers
- Test organization and naming conventions
Maintaining Consistency
Ensure new tests match existing style:
Using our established testing patterns, write tests for the new
payment processing feature. Follow our existing:
- File naming conventions (*.test.ts)
- Mock setup patterns
- Test data factories
- Error testing approaches
Real-World Example
Here's how this played out for a payment processing feature:
Phase 1: Test Planning and Writing
Help me design comprehensive tests for payment processing:
- Valid payment scenarios
- Invalid card data handling
- Network timeout handling
- Fraud detection integration
- Refund processing
- Currency conversion edge cases
Phase 2: Test Implementation
AI generated 47 test cases covering all scenarios, including edge cases like:
- Partial network failures
- Currency precision issues
- Race conditions in concurrent payments
- Invalid Unicode in payment descriptions
Phase 3: Commit Tests
All failing tests were committed with clear descriptions of expected behavior.
Phase 4: Implementation
AI implemented payment processing to satisfy all tests without modifying a single test case.
Phase 5: Verification
Independent security review caught two issues that were fixed while maintaining test compliance.
Benefits Observed
Using this approach consistently resulted in:
- 40% fewer production bugs compared to implementation-first approaches
- Faster debugging due to comprehensive test coverage
- Better API design from thinking about usage patterns first
- Improved code maintainability through clear behavioral specifications
- Enhanced team confidence in AI-generated code
Getting Started
1. Pick a small feature (single function or class)
2. Write comprehensive failing tests with AI assistance
3. Commit the failing tests to establish the contract
4. Implement to pass tests without modifying them
5. Review and refactor while maintaining test compliance
Remember: Tests are your specification. Once committed, they define success. AI's job is to satisfy that specification, not redefine it.