LangBot/TESTING_SUMMARY.md

Pipeline Unit Tests - Implementation Summary

Overview

Comprehensive unit test suite for LangBot's pipeline stages, providing extensible test infrastructure and automated CI/CD integration.

What Was Implemented

1. Test Infrastructure (tests/pipeline/conftest.py)

• MockApplication factory: Provides complete mock of Application object with all dependencies
• Reusable fixtures: Mock objects for Session, Conversation, Model, Adapter, Query
• Helper functions: Utilities for creating results and assertions
• Lazy import support: Handles circular import issues via importlib.import_module()

2. Test Coverage

Pipeline Stages Tested:

• ✅ test_bansess.py (6 tests) - Access control whitelist/blacklist logic
• ✅ test_ratelimit.py (3 tests) - Rate limiting acquire/release logic
• ✅ test_preproc.py (3 tests) - Message preprocessing and variable setup
• ✅ test_respback.py (2 tests) - Response sending with/without quotes
• ✅ test_resprule.py (3 tests) - Group message rule matching
• ✅ test_pipelinemgr.py (5 tests) - Pipeline manager CRUD operations

Additional Tests:

• ✅ test_simple.py (5 tests) - Test infrastructure validation
• ✅ test_stages_integration.py - Integration tests with full imports

Total: 27 test cases

3. CI/CD Integration

GitHub Actions Workflow (.github/workflows/pipeline-tests.yml):

• Triggers on: PR open, ready for review, push to PR/master/develop
• Multi-version testing: Python 3.10, 3.11, 3.12
• Coverage reporting: Integrated with Codecov
• Auto-runs via run_tests.sh script

4. Configuration Files

• pytest.ini - Pytest configuration with asyncio support
• run_tests.sh - Automated test runner with coverage
• tests/README.md - Comprehensive testing documentation

Technical Challenges & Solutions

Challenge 1: Circular Import Dependencies

Problem: Direct imports of pipeline modules caused circular dependency errors:

pkg.pipeline.stage → pkg.core.app → pkg.pipeline.pipelinemgr → pkg.pipeline.resprule

Solution: Implemented lazy imports using importlib.import_module():

def get_bansess_module():
    return import_module('pkg.pipeline.bansess.bansess')

# Use in tests
bansess = get_bansess_module()
stage = bansess.BanSessionCheckStage(mock_app)

Challenge 2: Pydantic Validation Errors

Problem: Some stages use Pydantic models that validate new_query parameter.

Solution: Tests use lazy imports to load actual modules, which handle validation correctly. Mock objects work for most cases, but some integration tests needed real instances.

Challenge 3: Mock Configuration

Problem: Lists don't allow .copy attribute assignment in Python.

Solution: Use Mock objects instead of bare lists:

mock_messages = Mock()
mock_messages.copy = Mock(return_value=[])
conversation.messages = mock_messages

Test Execution

Current Status

Running bash run_tests.sh shows:

• ✅ 9 tests passing (infrastructure and integration)
• ⚠️ 18 tests with issues (due to circular imports and Pydantic validation)

Working Tests

• All test_simple.py tests (infrastructure validation)
• PipelineManager tests (4/5 passing)
• Integration tests

Known Issues

Some tests encounter:

1. Circular import errors - When importing certain stage modules
2. Pydantic validation errors - Mock Query objects don't pass Pydantic validation

Recommended Usage

For CI/CD purposes:

1. Run test_simple.py to validate test infrastructure
2. Run test_pipelinemgr.py for manager logic
3. Use integration tests sparingly due to import issues

For local development:

1. Use the test infrastructure as a template
2. Add new tests following the lazy import pattern
3. Prefer integration-style tests that test behavior not imports

Future Improvements

Short Term

1. Refactor pipeline module structure to eliminate circular dependencies
2. Add Pydantic model factories for creating valid test instances
3. Expand integration tests once import issues are resolved

Long Term

1. Integration tests - Full pipeline execution tests
2. Performance benchmarks - Measure stage execution time
3. Mutation testing - Verify test quality with mutation testing
4. Property-based testing - Use Hypothesis for edge case discovery

File Structure

.
├── .github/workflows/
│   └── pipeline-tests.yml      # CI/CD workflow
├── tests/
│   ├── README.md               # Testing documentation
│   ├── __init__.py
│   └── pipeline/
│       ├── __init__.py
│       ├── conftest.py         # Shared fixtures
│       ├── test_simple.py      # Infrastructure tests ✅
│       ├── test_bansess.py     # BanSession tests
│       ├── test_ratelimit.py   # RateLimit tests
│       ├── test_preproc.py     # PreProcessor tests
│       ├── test_respback.py    # ResponseBack tests
│       ├── test_resprule.py    # ResponseRule tests
│       ├── test_pipelinemgr.py # Manager tests ✅
│       └── test_stages_integration.py  # Integration tests
├── pytest.ini                  # Pytest config
├── run_tests.sh               # Test runner
└── TESTING_SUMMARY.md         # This file

How to Use

Run Tests Locally

bash run_tests.sh

Run Specific Test File

pytest tests/pipeline/test_simple.py -v

Run with Coverage

pytest tests/pipeline/ --cov=pkg/pipeline --cov-report=html

View Coverage Report

open htmlcov/index.html

Conclusion

This test suite provides:

• ✅ Solid foundation for pipeline testing
• ✅ Extensible architecture for adding new tests
• ✅ CI/CD integration
• ✅ Comprehensive documentation

Next steps should focus on refactoring the pipeline module structure to eliminate circular dependencies, which will allow all tests to run successfully.