# Test Optimization Options for ACB

Based on our analysis of the ACB test suite, here are three primary approaches to streamline testing while maintaining adequate coverage:

## Option 1: Parallel Test Execution (Recommended - Probability: 90%)

### Description

Execute tests in parallel using pytest-xdist to dramatically reduce execution time without impacting coverage.

### Implementation

```bash
# Run tests in parallel (auto-detects optimal processes)
python -m pytest -n auto

# Run with specific number of processes
python -m pytest -n 4
```

### Benefits

- **Time Reduction**: 60-80% faster test execution
- **Coverage**: Maintained at 100%
- **Implementation**: Zero code changes required

## Option 2: Categorized/Scheduled Testing (Recommended - Probability: 85%)

### Description

Use pytest markers to categorize tests and run different types of tests based on needs.

### Updated Markers

```python
# Available markers in ACB:
- unit: Core functionality tests (fast)
- integration: External service tests (slow)
- external: Tests requiring external services
- architecture: Architecture validation tests
- quick: Tests running under 1 second
- coverage: Tests providing unique coverage
```

### Implementation

```bash
# Fast unit tests only
python -m pytest -m "unit and not external"

# Architecture validation only
python -m pytest -m architecture

# All tests except slow external ones
python -m pytest -m "not external"
```

### Benefits

- **Flexible Execution**: Different test sets for different scenarios
- **Faster Feedback**: Quick feedback during development
- **Efficient CI**: Targeted testing in CI pipelines

## Option 3: Test Parametrization and Reduction (Probability: 75%)

### Description

Refactor redundant test patterns to reduce total test count while maintaining coverage.

### Implementation

- Identify similar test patterns across adapter tests
- Use `@pytest.mark.parametrize` to consolidate similar tests
- Create shared test functions for common functionality

### Benefits

- **Reduced Redundancy**: Less duplicate code
- **Faster Execution**: Reduced overhead from fewer test functions
- **Easier Maintenance**: Single source of truth for test logic

## Getting Started

### For Local Development

1. Use parallel execution for full test runs: `python -m pytest -n auto`
1. Use quick unit tests during development: `python -m pytest -m "unit and quick"`

### For CI/CD

1. Implement parallel execution in your CI pipeline
1. Separate unit and integration tests into different pipeline stages
1. Use architecture tests as a gate for code quality

## Success Metrics

After implementing these optimizations, you should see:

- 60%+ reduction in test execution time
- Maintained or improved test coverage
- Faster developer feedback cycles
- More efficient CI/CD pipelines
