How to create a test ecosystem

As a QA Engineer, I have developed a robust test ecosystem that ensures the reliability and efficiency of our software. This ecosystem comprises multiple layers of testing, supported by shared libraries and exported modules. Below, I will explain the architecture, its components, and how they interact to maintain a high-quality standard in our software testing strategy.

The test ecosystem consists of four top-level testing strategies:

• API Tests
• Performance Tests
• End-to-End (E2E) Tests
• Manual Load Tests

These top-level tests leverage a set of shared libraries and exported modules to facilitate efficient test execution and data management.

Shared Libraries (LIBS)
These are essential utilities and tools that provide core functionalities:

• QA-Utils: A collection of utilities for authentication, database connections, and entity management.
• DB Operations: Handles database interactions including seeding and querying.
• Massive Data Generator: Automates large-scale data generation for performance and load tests.

Exported Modules
These modules encapsulate specific functionalities and are consumed across different tests:

• DB Operations - Seeder: Inserts test data into the database.
• DB Operations - Queries: Validates and retrieves test data from the database.
• QA-Utils - Entities: Defines data structures used across different tests.
• QA-Utils - DB Connection: Manages the database connection as a singleton instance.
• QA-Utils - Cognito Auth: Handles authentication using AWS Cognito.

Each type of test consumes specific modules based on its requirements.

API Tests

1. Utilize QA-Utils - Cognito Auth to validate user tokens.
2. Use DB Operations - Seeder to insert initial test data.
3. Leverage DB Operations - Queries to validate test results in the database.

Performance Tests

1. Utilize QA-Utils - Cognito Auth to validate user tokens.
2. Use Massive Data Generator to create initial test data for performance evaluation.

E2E Tests

1. Utilize QA-Utils - Cognito Auth to validate user tokens.
2. Use DB Operations - Seeder to create initial test data required for automated workflows.

Manual Load Tests

1. Use Massive Data Generator to create the necessary volume of test data to simulate real-world load conditions.

To streamline data creation and validation, the following internal dependencies exist:

Massive Data Generator relies on the Seeder module to insert bulk data efficiently.

Seeder consumes both DB Connection and Entities from QA-Utils to maintain consistency and reliability in test data setup.

Conclusion

This well-structured test ecosystem ensures that testing is automated, scalable, and efficient. By modularizing key functionalities and utilizing shared libraries, we achieve a seamless integration between different types of tests. This approach not only reduces redundancy but also enhances maintainability, making our software testing process robust and future-proof.