Test Data Management

Effective test data management is critical for robust and reliable testing of the Gemforce platform, encompassing smart contracts, cloud functions, and APIs. This document outlines strategies and best practices for creating, maintaining, and utilizing test data to ensure comprehensive test coverage and repeatable results.

1. Principles of Test Data Management

  • Realism: Test data should closely mimic production data characteristics (e.g., data types, distributions, relationships) without including actual sensitive production information.
  • Anonymization/Masking: Sensitive data from production environments must be properly anonymized or masked before being used in non-production testing environments.
  • Variety and Sufficiency: Ensure a sufficient variety and quantity of data to cover all positive, negative, edge, and boundary test cases.
  • Isolation: Test data for one test case or suite should not interfere with other test cases. Each test run should start with a known, consistent data state.
  • Version Control: Test data definitions and generation scripts should be managed under version control alongside the source code.
  • Refreshability: Test environments should be easily refreshable to a clean state with consistent test data.
  • Security: Test data, especially if it contains synthetic sensitive information, must be protected with appropriate access controls and encryption.

2. Types of Test Data

  • Structured Data: Data in predefined formats such as database records (MongoDB documents, PostgreSQL rows), smart contract state variables (e.g., mapping entries), or JSON payloads.
  • Unstructured Data: Data that doesn't fit a predefined schema, like logs, text descriptions, or media files that might be part of IPFS or other storage.
  • Configuration Data: Values used to configure environments, features, or external integrations (e.g., API keys, endpoint URLs).
  • Blockchain-Specific Data:
    • Wallet Addresses: Test private keys and addresses (e.g., Hardhat accounts).
    • Transaction Hashes: Simulate successful and failed transaction hashes.
    • Block Numbers/Timestamps: Data dependent on blockchain time.
    • Contract States: Specific states of deployed smart contracts for pre/post conditions.

3. Test Data Generation Strategies

3.1. Manual Creation

  • Use Case: Suited for small, specific test sets (e.g., a few valid/invalid inputs) or complex scenarios that are hard to automate.
  • Best Practice: Document clear instructions for manual data setup within the test case specifications.

3.2. Scripted Generation

  • Use Case: Ideal for repeatable setup of common scenarios, especially for smart contract or API integration tests.
  • Implementation:
    • Smart Contracts: Use deployment scripts (e.g., Hardhat scripts) to deploy contracts, mint tokens, set initial state, or configure parameters before running tests. ethers.js or web3.js can be used within Node.js scripts to directly interact with contracts.
    • Cloud Functions/APIs: Use Node.js/Python scripts to populate databases (MongoDB, PostgreSQL) with initial data using ORMs or direct database drivers. These scripts can be part of the test setup phase.

3.3. Data Seeding/Fixtures

  • Use Case: Populating a database or contract with a known, baseline set of data for a suite of tests.
  • Implementation: Create functions or scripts that "seed" the database or blockchain with predetermined data. This ensures each test run starts from a consistent state.
    • Example (Hardhat/Foundry): A fixture function that deploys contracts and sets up common scenarios, which can then be reused across multiple tests.
    • Example (Parse Server): Use Parse Server's Parse.Object APIs within seed scripts to create and populate classes.

3.4. Randomized Data Generation

  • Use Case: Generating large volumes of data for performance, stress, or fuzz testing.
  • Tools: Libraries like faker.js (JavaScript), Faker (Python), or custom random generators can create varied data.
  • Caution: Ensure randomly generated data still adheres to expected formats and constraints to avoid irrelevant failures.

3.5. Production Data Anonymization/Subset

  • Use Case: For testing scenarios that closely mirror real-world usage patterns, but without exposing sensitive information.
  • Process: Extract a subset of production data, and then apply anonymization, masking, or scrambling techniques to sensitive fields.
  • Caution: This is a complex process requiring careful planning to maintain data integrity and referential relationships while ensuring privacy.

4. Test Data Management for Gemforce Components

4.1. Smart Contracts

  • Hardhat/Foundry Test Suites:
    • Use beforeEach hooks or fixtures to deploy fresh contracts and set initial states for each test or test suite.
    • Utilize Hardhat's network helpers (evm_snapshot, evm_revert) to quickly reset the blockchain state to a known baseline between tests.
    • Define named accounts (e.g., deployer, alice, bob) for consistent test wallet addresses.
    • For testing specific scenarios, mock external contracts or interfaces when direct interaction is not required.
  • Merkle Tree Data: For whitelisting or Merkle proof-based features, pre-generate Merkle trees and proofs within test setup.

4.2. Cloud Functions & APIs

  • Database Seeding: Develop scripts to insert, update, and delete documents/rows in MongoDB/PostgreSQL to establish clean test states.
  • API Client Setup: Use test frameworks (e.g., Jest, Mocha, Pytest) that can configure HTTP clients to interact with your Parse Server or custom API endpoints.
  • Mocking External Services: Use mocking libraries (e.g., jest-fetch-mock, axios-mock-adapter, unittest.mock in Python) to simulate responses from external services (DFNS, Bridge API, third-party APIs) during testing, avoiding real external calls.

4.3. Configuration Data

  • Maintain environment-specific configuration files (e.g., .env.test, config.test.js) for different testing stages.
  • Use libraries like dotenv to load environment variables for tests.

5. Security Considerations

  • Never use real production private keys or sensitive API credentials in test environments.
  • Store test data securely, especially if it mimics sensitive information.
  • Ensure test data creation/deletion does not expose vulnerabilities (e.g., by creating overly permissive accounts).
  • Regularly review and sanitize test data to prevent accidental exposure of sensitive information.

By applying these test data management principles and strategies, developers can build a more robust, efficient, and secure testing framework for the Gemforce platform.