baseline-generator 1.1.0

A Python package for generating and managing JSON test baselines

Baseline Generator

A Python package for generating and managing JSON test baselines with ease.

Features

• 🏗️ Generate baselines - Create new baseline files with data validation
• ✅ Test against baselines - Compare test data with baselines and get detailed difference reports
• 🎯 CLI Tool - Command-line interface for easy baseline management

Installation

# Clone the repository
git clone https://github.com/alejandrogonzalvogonzalvo/baseline-generator.git
cd baseline-generator

# Install with Poetry
poetry install

Usage

Python API

from baseline_generator import BaselineGenerator, BaselineComparisonError, BaselineNotFoundError

# Initialize with default test folder
generator = BaselineGenerator()

# Or specify a custom test folder
generator = BaselineGenerator("my_test_folder")

# Generate a new baseline
test_data = {"expected": "result", "values": [1, 2, 3]}
generator.generate_baseline("new_baseline", test_data)

# Overwrite existing baseline
generator.generate_baseline("existing_baseline", test_data, overwrite=True)

# Test data against a baseline
try:
    generator.test_against_baseline("my_baseline", test_data)
    print("✓ Test passed - data matches baseline")
except BaselineComparisonError as e:
    print(f"❌ Test failed: {e.message}")
    for diff in e.differences:
        print(f"  - {diff}")
except BaselineNotFoundError as e:
    print(f"⚠️  Baseline created: {e.message}")

Command Line Interface

# Check if a baseline exists
baseline-generator check my_baseline

# Generate a baseline from JSON file
baseline-generator generate my_baseline data.json

# Load and display a baseline
baseline-generator load my_baseline

# Test data against a baseline
baseline-generator test my_baseline test_data.json

# Use custom test folder
baseline-generator --test-folder custom_tests check my_baseline

# Overwrite existing baseline
baseline-generator generate my_baseline data.json --overwrite

# Don't auto-create missing baselines during testing
baseline-generator test my_baseline test_data.json --no-create

Testing Against Baselines

The core functionality of this package is testing data against established baselines:

Automatic Baseline Creation

When testing against a non-existent baseline, it will automatically be created:

$ poetry run baseline-generator test new_test test_data.json
Warning: Baseline 'new_test.json' did not exist and was created.
Review the generated baseline and re-run the test.

Detailed Difference Reporting

When data doesn't match the baseline, you get detailed difference reports:

$ poetry run baseline-generator test api_test modified_data.json
❌ Test data does not match baseline 'api_test.json'

Differences found:
  1. api_response.data.users: List length mismatch - baseline: 2, test: 3
  2. api_response.data.users[1].name: Value mismatch - baseline: 'Bob', test: 'Charlie'
  3. api_response.data.total_count: Value mismatch - baseline: 2, test: 3
  4. api_response.metadata.timestamp: Value mismatch - baseline: '2024-01-01T10:00:00Z', test: '2024-01-01T11:00:00Z'
  5. api_response.metadata.version: Value mismatch - baseline: '1.0.0', test: '1.1.0'

Total differences: 5

Difference Types Detected

• Value mismatches: Different primitive values
• Type mismatches: Different data types (string vs number, etc.)
• Missing keys: Keys present in baseline but missing in test data
• Extra keys: Keys in test data not present in baseline
• List length mismatches: Different array lengths
• Nested structure differences: Deep comparison of complex objects

Development

Code Quality Tools

This project enforces high code quality standards:

# Run tests
poetry run pytest

# Type checking
poetry run mypy baseline_generator

# Code formatting
poetry run black baseline_generator tests

# Import sorting
poetry run isort baseline_generator tests

# All checks together
poetry run pytest && poetry run mypy baseline_generator && poetry run black --check baseline_generator tests && poetry run isort --check-only baseline_generator tests

CI/CD and Publishing

• Automated Releases (.github/workflows/release.yml):
  • Triggered on pushes to main branch
  • Automatically determines semantic version bumps
  • Generates changelog entries
  • Creates GitHub releases
  • Publishes to PyPI

Configuration

The project uses pyproject.toml for all tool configuration:

• Poetry: Dependency management and packaging
• mypy: Strict type checking with enforced type hints
• Black: Code formatting (88 character line length)
• isort: Import sorting (Black-compatible)
• pytest: Testing with coverage reporting
• Coverage: HTML and terminal coverage reports

Requirements

• Python 3.12
• Poetry for dependency management

License

MIT License