# ACB Test Suite Optimization: Streamlining Tests Without Losing Coverage

## Current State Analysis

Based on analysis of the ACB codebase, the test suite currently includes:

- 196 test files
- 3,417 collected tests
- Overall coverage of 26% of the codebase
- Tests take considerable time to run due to setup overhead and external service dependencies

## Option 1: Parallel Test Execution with pytest-xdist (High Priority)

### Overview

Use `pytest-xdist` to run tests in parallel across multiple processes, significantly reducing execution time while maintaining full coverage.

### Implementation

1. Install `pytest-xdist`: already included in dependencies
1. Run tests with: `python -m pytest -n auto` (auto-detects optimal number of processes)
1. Configure in `pyproject.toml` with appropriate worker distribution

### Benefits

- Reduces test execution time by 60-80% on multi-core systems
- Maintains full test coverage
- No changes needed to existing test code

### Considerations

- Need to handle test isolation carefully (already implemented via `reset_dependency_container` fixture)
- Some tests may require single process execution (marked with benchmark marker)

### Probability of Success: 90%

### Time Savings: 60-80%

### Coverage Impact: 0%

## Option 2: Smart Test Selection and Categorization (High Priority)

### Overview

Implement a more refined test classification system to allow selective execution of different test types based on needs.

### Implementation

1. Create additional pytest markers:

   - `@pytest.mark.unit` - core functionality tests (fast)
   - `@pytest.mark.integration` - external service tests (slow)
   - `@pytest.mark.architecture` - architecture validation tests
   - `@pytest.mark.quick` - tests that run in under 1 second
   - `@pytest.mark.coverage` - tests that provide unique coverage

1. Update `conftest.py` to include these markers

1. Create specific CI pipelines for different test categories

### Execution Examples

- `python -m pytest -m "unit and not external"` - Run fast unit tests only
- `python -m pytest -m "coverage"` - Run tests that provide unique coverage
- `python -m pytest -m "architecture"` - Run architecture validation tests only

### Benefits

- Allows for targeted testing in different scenarios (local development vs CI)
- Fast feedback on critical functionality during development
- More efficient CI pipelines

### Probability of Success: 85%

### Time Savings: 50-90% depending on selected category

### Coverage Impact: -5% to +5% depending on selection strategy

## Option 3: Test Caching and Incremental Execution (Medium Priority)

### Overview

Implement a smart caching system that only runs tests affected by code changes, using tools like `pytest-testmon` or `pytest-monitor`.

### Implementation

1. Integrate `pytest-monitor` for code-coverage correlation
1. Implement git-based change detection to determine which tests to run
1. Create a mapping system between code modules and relevant tests

### Benefits

- Significantly reduces time for local development iterations
- Only run tests that could be affected by changes
- Maintain overall coverage through scheduled full test runs

### Considerations

- Risk of missing edge cases if correlation is imperfect
- Requires careful implementation to avoid false confidence
- More complex setup and maintenance

### Probability of Success: 70%

### Time Savings: 70-95% for incremental runs

### Coverage Impact: -10% for incremental, 0% for full runs

## Option 4: Test Composition and Parametrization (Medium Priority)

### Overview

Refactor existing tests to reduce redundancy through parametrization and composition, reducing the total number of test functions while maintaining coverage.

### Implementation

1. Identify redundant test patterns in adapter tests
1. Use `@pytest.mark.parametrize` for similar test scenarios
1. Create common test functions that can be reused across multiple test classes

### Example Implementation

```python
# Instead of individual tests for each adapter
@pytest.mark.parametrize("adapter_type", ["memory", "redis", "file"])
def test_cache_operations(adapter_type):
    # Common cache operations test
    pass
```

### Benefits

- Reduces code duplication
- Easier maintenance
- Faster test execution due to reduced overhead

### Probability of Success: 75%

### Time Savings: 10-30%

### Coverage Impact: 0%

## Option 5: Selective External Service Mocking (Low Priority)

### Overview

Improve mocking strategies to eliminate the need for external services in most tests, reducing flakiness and execution time.

### Implementation

1. Enhance existing mocking fixtures for external services
1. Create more comprehensive mock implementations
1. Reduce use of real external services to only essential integration tests

### Benefits

- Eliminates external dependencies for most tests
- Increases reliability and speed
- Better test isolation

### Considerations

- Risk of losing real integration validation
- Requires careful mock implementation to maintain realism
- Some tests may need actual service interaction to be meaningful

### Probability of Success: 80%

### Time Savings: 50-70% (for external service tests)

### Coverage Impact: 0% (with proper mocks)

## Recommended Implementation Strategy

### Phase 1 (Immediate - 1 week)

- Implement Option 1 (Parallel Execution): Highest impact, lowest risk
- Update CI configuration to use parallel execution

### Phase 2 (2-3 weeks)

- Implement Option 2 (Smart Test Selection): Add markers and categories
- Update documentation and developer workflows

### Phase 3 (4-6 weeks)

- Implement Option 4 (Test Parametrization): Reduce redundancy
- Evaluate Option 3 (Caching) for local development

## Risk Mitigation

1. Maintain a full test suite execution in CI to ensure no coverage loss
1. Use probabilistic testing for high-confidence fast feedback
1. Implement alerting for coverage drops
1. Regular full test runs (daily/weekly) even with incremental strategies

## Success Metrics

- Test execution time reduction: Target 60%+ reduction
- Coverage maintenance: No net reduction in coverage
- Developer productivity: Faster feedback cycles
- CI cost reduction: Through more efficient execution
