test-data-generator 0.1.1

Deterministic test data generator from JSON schemas for consistent, repeatable testing

Test Data Generator

A deterministic test data generator that creates consistent, repeatable test data from JSON schemas. Perfect for writing reliable tests that produce the same results every time.

Features

• ✨ Deterministic Generation - Same seed always produces identical data
• 📝 Flexible Input - Accept schemas as file paths, JSON strings, or Python dicts
• 🎯 Type-Rich - Support for strings, integers, floats, booleans, dates, emails, and UUIDs
• 🔧 Constraint Support - Min/max ranges, enums, and length constraints
• 🧪 Testing-First - Built specifically for creating consistent test fixtures
• 🚀 Zero Dependencies - Uses only Python standard library (except dev tools)

Installation

pip install test-data-generator

Quick Start

from test_data_generator import generate_test_data

# Define your schema
schema = {
    "fields": {
        "user_id": {"type": "uuid"},
        "username": {
            "type": "string",
            "constraints": {"min": 5, "max": 15}
        },
        "email": {"type": "email"},
        "age": {
            "type": "integer",
            "constraints": {"min": 18, "max": 100}
        },
        "active": {"type": "boolean"}
    }
}

# Generate test data
data = generate_test_data(schema, count=10, seed=42)

# Same seed = same data every time!
data1 = generate_test_data(schema, count=5, seed=42)
data2 = generate_test_data(schema, count=5, seed=42)
assert data1 == data2  # ✓ Always true

Schema Format

Schemas define the structure and constraints for generated data:

{
  "fields": {
    "field_name": {
      "type": "<type>",
      "constraints": {
        "min": 0,
        "max": 100,
        "enum": ["value1", "value2"],
        "format": "iso"
      }
    }
  }
}

Supported Types

• string - Random alphanumeric strings
• integer - Random integers
• float - Random floating-point numbers
• boolean - Random true/false values
• date - ISO format dates (2024-01-15)
• email - Valid email addresses
• uuid - UUID v4 identifiers

Constraints

Constraint	Applies To	Description
min	string, integer, float, date	Minimum value or length
max	string, integer, float, date	Maximum value or length
enum	all types	Pick from a fixed set of values
format	date	Date format: "iso" (default) or "timestamp"

Usage Examples

1. Using a Dictionary Schema

from test_data_generator import generate_test_data

schema = {
    "fields": {
        "name": {"type": "string"},
        "score": {"type": "integer", "constraints": {"min": 0, "max": 100}}
    }
}

data = generate_test_data(schema, count=5, seed=42)

2. Using a JSON String

schema_json = '''
{
    "fields": {
        "status": {
            "type": "string",
            "constraints": {"enum": ["active", "inactive", "pending"]}
        }
    }
}
'''

data = generate_test_data(schema_json, count=10)

3. Using a File Path

Create user_schema.json:

{
  "fields": {
    "user_id": { "type": "uuid" },
    "email": { "type": "email" },
    "created_at": { "type": "date" }
  }
}

Then use it:

data = generate_test_data("user_schema.json", count=20, seed=123)

4. Advanced: Using DataGenerator Class

For more control, use the DataGenerator class directly:

from test_data_generator import DataGenerator

schema = {
    "fields": {
        "value": {"type": "integer"}
    }
}

generator = DataGenerator(schema, seed=42)

# Generate batch 1
batch1 = generator.generate(5)

# Reset to regenerate same data
generator.reset_seed()
batch2 = generator.generate(5)

assert batch1 == batch2  # Same data again

# Or use a different seed
generator.reset_seed(seed=99)
batch3 = generator.generate(5)

assert batch1 != batch3  # Different data

Testing with Deterministic Data

The key benefit is writing tests with predictable data:

import pytest
from test_data_generator import generate_test_data

@pytest.fixture
def test_users():
    """Generate consistent test users for every test run."""
    schema = {
        "fields": {
            "user_id": {"type": "uuid"},
            "email": {"type": "email"},
            "age": {"type": "integer", "constraints": {"min": 18, "max": 65}}
        }
    }
    return generate_test_data(schema, count=10, seed=42)

def test_user_filtering(test_users):
    # test_users will be the SAME every time this test runs
    assert len(test_users) == 10
    # Write assertions based on the known deterministic data

Complex Schema Example

schema = {
    "fields": {
        "order_id": {"type": "uuid"},
        "customer_email": {"type": "email"},
        "order_date": {
            "type": "date",
            "constraints": {
                "min": "2024-01-01",
                "max": "2024-12-31"
            }
        },
        "status": {
            "type": "string",
            "constraints": {
                "enum": ["pending", "shipped", "delivered", "cancelled"]
            }
        },
        "total": {
            "type": "float",
            "constraints": {"min": 10.0, "max": 5000.0}
        },
        "is_priority": {"type": "boolean"}
    }
}

orders = generate_test_data(schema, count=100, seed=42)

Development

# Install in development mode
pip install -e .

# Run tests
pytest tests/ -v

# Run linter
ruff check src/

Requirements

• Python 3.14+
• No runtime dependencies (pure stdlib)
• Dev dependencies: pytest, ruff

Architecture

• load_schema.py - Schema loading from files/strings
• schema.py - Schema validation and utilities
• generators.py - Type-specific value generators
• generator.py - Core DataGenerator class with seeding
• __init__.py - Public API: generate_test_data() function

Why Deterministic?

Deterministic test data generation ensures:

• ✓ Tests are reproducible across different machines
• ✓ Failed tests can be debugged with the exact same data
• ✓ CI/CD pipelines get consistent results
• ✓ Team members see identical test behavior
• ✓ Easier to reason about test failures

License

MIT License