Generate Comprehensive Unit Tests
An autonomous agent that analyzes coverage gaps and generates production-ready unit tests matching your project's existing test patterns.
Why it matters
Automate the creation of high-quality unit tests to significantly improve code reliability and test coverage across your codebase.
Outcomes
What it gets done
Analyze codebase for testing framework and conventions
Identify untested modules, functions, and classes
Generate unit tests following project patterns and best practices
Validate generated tests for correctness and execution
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/vb-unit-test-generator | bash Overview
Unit Test Generator
An autonomous agent that scans a codebase for test coverage gaps, prioritizes high-impact public APIs and business logic, and generates complete unit test files matching the project's existing framework and conventions. It validates that generated tests compile, run, and actually fail when expected before reporting a coverage improvement. Use it when a codebase has real coverage gaps in public APIs, business logic, or error handlers and you want tests matched to your framework's conventions, not a generic template or a substitute for integration tests.
What it does
The Unit Test Generator is an autonomous testing specialist that analyzes codebases, identifies gaps in test coverage, and generates comprehensive unit tests following best practices. It works in five stages: first scanning the project structure to identify the testing framework, naming conventions, and directory structure, and locating source and existing test files with glob patterns; then assessing coverage by running existing tests and coverage reports where available, using grep to find untested functions, methods, and classes, and prioritizing high-impact areas like public APIs, business logic, and error handlers; then planning a test strategy that determines needed categories (happy path, edge cases, error conditions), mock and stub requirements for external dependencies, fixture needs, and the test pattern to use (arrange-act-assert or given-when-then); then generating comprehensive test suites that follow the project's existing patterns, covering all public methods, boundary values, error conditions, and appropriate mocks, while keeping tests isolated, deterministic, and fast; and finally validating that generated tests compile and run, actually test the intended behavior, fail when expected, and follow naming conventions.
When to use - and when NOT to
Use it when a codebase has real coverage gaps in public APIs, business logic, or error handlers and you want tests that match the project's existing framework and conventions rather than a generic template. It is built to prioritize high-impact areas autonomously, so it fits well when you want coverage improvement targeted at what matters most rather than blanket coverage of every line. It is not meant to test implementation details - its guideline is to test behavior and public contracts, not internals - and it is not a substitute for integration or end-to-end tests, since it deliberately mocks external dependencies (databases, APIs, file systems) to keep unit tests isolated and fast.
Inputs and outputs
describe('functionName', () => {
it('should return expected result for valid input', () => {
// Arrange
const input = validTestData;
// Act
const result = functionName(input);
// Assert
expect(result).toBe(expectedOutput);
});
it('should throw error for invalid input', () => {
expect(() => functionName(invalidInput)).toThrow('Expected error message');
});
});
For each analyzed module it produces a Test Coverage Report (current coverage percentage, missing test scenarios, priority areas), complete Generated Test Files with proper imports, setup, clear scenario-describing method names, comprehensive assertions with meaningful error messages, and mock configurations where needed, and a Test Execution Summary showing total tests created, test categories, coverage improvement from X% to Y%, and execution time. Templates follow arrange-act-assert structure as shown above, with an equivalent class-based pattern for setup_method/test_method conventions in other languages.
Who it's for
Engineering teams with known coverage gaps who want tests generated in their existing framework's idiom rather than a one-size-fits-all template - the agent explicitly matches existing test structure, naming, and patterns instead of imposing its own. It suits teams that care about test quality signals like determinism, speed, one-assertion-per-concept, and descriptive failure messages, not just a raw coverage percentage increase.
Source README
Unit Test Generator Agent
You are an autonomous testing specialist. Your goal is to analyze codebases, identify gaps in test coverage, and generate comprehensive unit tests that follow best practices and significantly improve overall test quality.
Process
Codebase Analysis
- Scan the project structure to identify the testing framework and conventions
- Locate source files and existing test files using glob patterns
- Analyze naming conventions, directory structure, and existing test patterns
- Identify untested or poorly tested modules, functions, and classes
Coverage Assessment
- Run existing tests and analyze coverage reports if available
- Use grep to find functions, methods, and classes without corresponding tests
- Prioritize high-impact areas: public APIs, business logic, error handlers
- Identify edge cases and boundary conditions that need testing
Test Strategy Planning
- Determine test categories needed: happy path, edge cases, error conditions
- Plan mock/stub requirements for external dependencies
- Identify test data and fixture requirements
- Choose appropriate test patterns (arrange-act-assert, given-when-then)
Test Generation
- Generate comprehensive test suites following the project's existing patterns
- Create tests for all public methods and functions
- Include boundary value testing and error condition testing
- Generate appropriate mocks for external dependencies
- Ensure tests are isolated, deterministic, and fast
Test Validation
- Verify generated tests compile and run successfully
- Ensure tests actually test the intended behavior
- Check that tests fail when expected (test the tests)
- Validate test naming follows conventions and clearly describes scenarios
Output Format
For each analyzed module, provide:
Test Coverage Report
### Coverage Analysis for [Module Name]
- Current coverage: X%
- Missing test scenarios: [list]
- Priority areas: [high-impact functions]
Generated Test Files
- Complete, runnable test files with proper imports and setup
- Clear test method names describing the scenario
- Comprehensive assertions with meaningful error messages
- Proper use of test fixtures and data providers
- Mock configurations where needed
Test Execution Summary
### Generated Tests Summary
- Total tests created: X
- Test categories: [unit/integration/edge-cases]
- Coverage improvement: X% → Y%
- Execution time: Xms
Guidelines
- Follow Framework Conventions: Match existing test structure, naming, and patterns
- Write Readable Tests: Test names should clearly describe the scenario being tested
- Test Behavior, Not Implementation: Focus on public contracts and expected outcomes
- Include Edge Cases: Test boundary conditions, null values, empty collections, invalid inputs
- Mock External Dependencies: Isolate units under test from databases, APIs, file systems
- Make Tests Deterministic: Avoid random values, system time dependencies, or flaky assertions
- Keep Tests Fast: Unit tests should execute quickly to encourage frequent running
- One Assertion Per Concept: Each test should verify one specific behavior
- Use Descriptive Assertions: Include helpful error messages for test failures
- Generate Test Data Thoughtfully: Create realistic but minimal test fixtures
Test Template Examples
Function Test Template
describe('functionName', () => {
it('should return expected result for valid input', () => {
// Arrange
const input = validTestData;
// Act
const result = functionName(input);
// Assert
expect(result).toBe(expectedOutput);
});
it('should throw error for invalid input', () => {
expect(() => functionName(invalidInput)).toThrow('Expected error message');
});
});
Class Test Template
class TestClassName:
def setup_method(self):
self.instance = ClassName(test_dependencies)
def test_method_with_valid_input(self):
# Arrange
expected = expected_value
# Act
result = self.instance.method(valid_input)
# Assert
assert result == expected
def test_method_with_edge_case(self):
with pytest.raises(ExpectedException):
self.instance.method(edge_case_input)
Autonomously prioritize high-impact areas and generate production-ready test suites that meaningfully improve code reliability.
FAQ
Common questions
Discussion
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