Test Generation Workflow
Use AI to generate comprehensive test suites that cover edge cases humans might miss.
Overview
The Test Generation Workflow leverages AI to create thorough test coverage for your codebase. By analyzing function signatures, business logic, and code paths, AI can generate unit tests, integration tests, and edge case scenarios that would take humans significantly longer to write.
Problem
Writing comprehensive tests is time-consuming and often deprioritized: - Developers skip tests due to time pressure - Edge cases are frequently overlooked - Test coverage is inconsistent across the codebase - Maintaining tests as code evolves is burdensome
Solution
Integrate AI-powered test generation into your development workflow: - Generate initial test scaffolding from function signatures - Identify edge cases through code analysis - Create data fixtures and mocks automatically - Maintain test coverage as code changes AI handles the repetitive aspects of test writing while developers focus on verifying business logic correctness.
Implementation
Code Examples
import { calculateDiscount } from './pricing';
describe('calculateDiscount', () => {
// Happy path tests
describe('valid inputs', () => {
it('applies percentage discount correctly', () => {
expect(calculateDiscount(100, { type: 'percentage', value: 10 }))
.toBe(90);
});
it('applies fixed discount correctly', () => {
expect(calculateDiscount(100, { type: 'fixed', value: 15 }))
.toBe(85);
});
});
// Edge cases
describe('edge cases', () => {
it('handles zero price', () => {
expect(calculateDiscount(0, { type: 'percentage', value: 10 }))
.toBe(0);
});
it('prevents negative final price', () => {
expect(calculateDiscount(10, { type: 'fixed', value: 20 }))
.toBe(0);
});
it('caps percentage at 100%', () => {
expect(calculateDiscount(100, { type: 'percentage', value: 150 }))
.toBe(0);
});
});
// Error handling
describe('error handling', () => {
it('throws on invalid discount type', () => {
expect(() => calculateDiscount(100, { type: 'invalid' as any, value: 10 }))
.toThrow('Invalid discount type');
});
});
});AI-generated tests cover happy paths, edge cases, and error conditions systematically.
Best Practices
- Always review generated tests for logical correctness
- Use AI to identify edge cases, but verify they are relevant
- Maintain a balance between generated and hand-written tests
- Update test prompts as your testing patterns evolve
- Use generated tests as a starting point, not the final product
Considerations
- • Dramatically faster test creation
- • More comprehensive edge case coverage
- • Consistent test structure across the codebase
- • Lower barrier to achieving high test coverage
- • Tests serve as additional documentation
- • Generated tests may test implementation not behavior
- • Risk of false confidence from passing but shallow tests
- • AI may not understand business logic nuances
- • Generated mocks might not reflect real dependencies
- • Maintenance overhead if tests are too coupled to implementation