Tests are insurance. Everyone knows they should have them. Many projects don't. The gap between knowing and doing is filled with tight deadlines, shipping pressure, and the perpetual promise to "add tests later."
Later rarely comes. And when it does - when a production bug finally forces the issue - writing tests for existing code is miserable work. You have to understand code you didn't write (or wrote so long ago you've forgotten it), identify edge cases after the fact, and test behavior you're not entirely sure is correct.
AI agents can generate tests from your existing code. They analyze implementations, infer expected behavior, and create test suites that would take humans days to write. Not perfect tests - no automation produces perfect anything - but meaningful tests that catch real bugs and dramatically increase coverage.
Why Test Writing Gets Skipped
Understanding why testing is neglected helps understand why automation matters.
The Time Investment
Writing tests takes time. Not just a little time - often as much time as writing the implementation. A function that takes an hour to write might need an hour of test writing. That's a 2x multiplier on development time that schedules don't accommodate.
The Delayed Payoff
Tests pay off later. They catch bugs during refactoring. They validate behavior during upgrades. They prevent regressions in the future. But today, when you're trying to ship a feature, those benefits feel distant and hypothetical.
The Knowledge Requirement
Good tests require understanding edge cases, boundary conditions, and failure modes. You need to think adversarially about your own code. This mental shift is valuable but demanding.
The Maintenance Burden
Tests need updating when code changes. Outdated tests that fail for the wrong reasons become noise. Tests that pass when they shouldn't become dangerous. Maintaining tests is ongoing work.
The Skill Gap
Writing good tests is a skill. Not everyone has it. Teams without testing expertise struggle to write tests that actually catch bugs versus tests that just increase coverage numbers.
Automation addresses all of these by making test generation fast, comprehensive, and consistent.
Types of Tests to Automate
Different test types serve different purposes.
Unit Tests
Test individual functions in isolation:
@devonair generate unit tests for all functions in /src/utils
@devonair create unit tests for the UserService class with edge cases
Unit tests are the foundation of any test suite.
Integration Tests
Test components working together:
@devonair generate integration tests for the user registration flow
@devonair create tests that verify the API endpoints work with the database layer
Snapshot Tests
Capture and compare output:
@devonair generate Jest snapshot tests for all React components in /src/components
@devonair create snapshot tests for API response shapes
Property-Based Tests
Test invariants across many inputs:
@devonair generate property-based tests for the calculation functions
@devonair create fuzz tests for input parsing functions
End-to-End Tests
Test complete user flows:
@devonair generate Playwright tests for the checkout flow
@devonair create Cypress tests for user authentication scenarios
Test Generation Strategies
Coverage-Driven Generation
Start with coverage gaps:
@devonair analyze test coverage and generate tests for uncovered code paths
@devonair identify functions with less than 80% coverage and add missing tests
Implementation-Driven Generation
Generate tests from existing code:
@devonair generate tests for /src/services based on current implementations
The agent reads the code, understands what it does, and creates tests that verify that behavior.
Specification-Driven Generation
Generate tests from documentation:
@devonair generate tests from the API specification in openapi.yaml
@devonair create tests based on the requirements in /docs/features/auth.md
Example-Driven Generation
Generate tests from examples:
@devonair generate additional test cases similar to existing tests in /tests/user.test.js
@devonair expand test coverage using the style and patterns from /tests/examples
Generating Meaningful Tests
Not all tests are valuable. Automated tests should catch real bugs.
Edge Cases
@devonair generate tests covering edge cases: null inputs, empty arrays, boundary values
Edge cases catch bugs that happy-path testing misses.
Error Conditions
@devonair create tests for error handling paths in /src/api
@devonair generate tests that verify proper exception throwing and catching
Boundary Conditions
@devonair generate tests for boundary conditions: max values, min values, off-by-one scenarios
Race Conditions
@devonair create tests for concurrent access scenarios in the caching layer
@devonair generate tests to detect potential race conditions in async code
Type Edge Cases
@devonair create tests with various type coercions and edge cases
JavaScript's type flexibility creates many edge cases worth testing.
Framework-Specific Generation
Jest
@devonair generate Jest tests for /src with describe blocks, beforeEach setup, and proper mocking
@devonair create Jest tests with mock implementations for external dependencies
Pytest
@devonair generate pytest tests for /src with fixtures and parametrized tests
@devonair create pytest tests using factory_boy for test data
Mocha/Chai
@devonair generate Mocha tests with Chai assertions for /src/utils
React Testing Library
@devonair generate React Testing Library tests focusing on user behavior
@devonair create tests that verify accessibility in React components
Playwright/Cypress
@devonair generate Playwright tests for critical user journeys
@devonair create Cypress tests with proper waiting and retry strategies
Test Organization
Well-organized tests are maintainable tests.
File Structure
@devonair generate tests following the convention: test files next to source files
@devonair create tests in /tests mirror structure matching /src
Naming Conventions
@devonair generate tests with descriptive names: should_return_error_when_user_not_found
@devonair use given-when-then naming in test descriptions
Test Grouping
@devonair organize tests by feature with nested describe blocks
@devonair group tests by behavior: creation, updates, deletion, queries
Shared Fixtures
@devonair create shared test fixtures in /tests/fixtures for reuse
@devonair generate factory functions for test data creation
Handling Dependencies
Real code has dependencies that tests must handle.
Mocking
@devonair generate tests with proper mocks for external API calls
@devonair create mock implementations for database interactions
Stubbing
@devonair generate tests with stubbed time-dependent functions
@devonair create stubs for file system operations in tests
Test Doubles
@devonair generate spy functions to verify interaction patterns
Dependency Injection
@devonair refactor code to support dependency injection for better testability
Sometimes the agent suggests code changes to make testing easier.
Coverage Improvement
Coverage Analysis
@devonair analyze test coverage and identify critical gaps
@devonair report on coverage by directory and highlight untested areas
Targeted Generation
@devonair generate tests to achieve 80% line coverage in /src/core
@devonair add tests for uncovered branches in conditional logic
Coverage Maintenance
@devonair on PR: verify new code has test coverage above threshold
@devonair schedule weekly: report on coverage changes and trends
Test Quality Metrics
Coverage isn't the only metric.
Mutation Testing
@devonair run mutation testing to evaluate test effectiveness
@devonair improve tests based on mutation testing results
Mutation testing reveals tests that pass when they shouldn't.
Test Speed
@devonair identify slow tests and suggest performance improvements
@devonair refactor tests to reduce I/O and improve speed
Test Reliability
@devonair identify flaky tests and fix timing-related issues
@devonair remove test interdependencies that cause random failures
Maintaining Generated Tests
Tests need updates as code evolves.
Test Updates
@devonair update tests in /tests to reflect changes in /src
@devonair fix broken tests after refactoring
Test Cleanup
@devonair remove tests for deleted code
@devonair identify and remove duplicate test cases
Test Modernization
@devonair update tests to use current testing patterns and assertions
@devonair migrate tests from enzyme to React Testing Library
Testing Legacy Code
Untested legacy code benefits most from automation.
Characterization Tests
@devonair create characterization tests that document current behavior of /src/legacy
Characterization tests capture what code does, not what it should do. They're essential for safely refactoring legacy code.
Coverage Bootstrapping
@devonair add basic test coverage to /src/legacy starting with public interfaces
@devonair generate tests for the most-changed files in the last 6 months
Prioritize testing code that changes frequently.
Incremental Improvement
@devonair on PR: require tests for any changes to /src/legacy
New changes to legacy code come with tests.
Test-Driven Workflows
Integrate testing into development.
PR Requirements
@devonair on PR: verify tests exist for new functions and classes
@devonair on PR: ensure test coverage doesn't decrease
Pre-Commit Testing
@devonair on PR: run affected tests and report results
@devonair on commit: validate tests pass before allowing commit
Documentation
@devonair add test documentation explaining test strategies and patterns
Help future developers understand and maintain tests.
Getting Started
Start with high-value, low-risk areas:
@devonair generate unit tests for /src/utils with common edge cases
Utility functions are good starting points - they're usually pure functions with clear inputs and outputs.
Review the generated tests. Do they make sense? Do they catch real bugs?
Then expand:
@devonair generate tests for /src/services with mocked dependencies
Set up ongoing generation:
@devonair on PR: suggest tests for new untested code paths
A test suite that grows automatically is a test suite that actually exists. Stop promising to add tests later - start generating them now.
FAQ
Are generated tests as good as hand-written tests?
Generated tests cover more ground faster. Hand-written tests may capture domain knowledge better. The ideal is generated tests supplemented with hand-written tests for complex scenarios. Some coverage is infinitely better than no coverage.
Won't generated tests just test the implementation?
The agent generates tests based on intended behavior, not just current implementation. It uses function names, documentation, and context to infer what code should do. For critical paths, review and adjust generated tests to ensure they test requirements, not accidents.
How do I handle tests that are too coupled to implementation?
When generated tests break from valid refactoring, they're testing implementation, not behavior. Ask:
@devonair refactor tests in /tests/utils to test behavior rather than implementation details
What about testing non-deterministic code?
Non-deterministic code (random values, current time, external services) needs special handling:
@devonair generate tests for time-dependent code with mocked Date functions
The agent knows to inject test doubles for non-deterministic dependencies.