[QA.TEM.2] Ensure consistent test case execution using test beds
Category: FOUNDATIONAL
Test cases require specific conditions and test data to run in a predetermined state. Test beds, configured within broader testing environments, provide the conditions necessary to ensure reproducible and accurate test case execution. While a single testing environment, such as a staging environment, can host multiple test beds, each test bed is tailored with the infrastructure and data suitable for specific test scenarios. Being able to start each test case with the correct configuration and data setup makes testing reliable, consistent, and confirms that anomalies or failures can be attributed to code changes rather than data inconsistencies.
Integrate test bed preparation into the delivery pipeline, leveraging infrastructure as code (IaC) to help guarantee consistent test bed setup. Rather than updating or patching test beds, use immutable infrastructure and treat them as ephemeral environments. When a test is run, create a new test bed using IaC tools to help ensure that it is clean and consistent. It is advantageous to have a fresh environment for each test. However, after running the tests, while the test bed can be deleted, it is important to avoid deleting logs and data that can aid with debugging the testing process. This data may be required for analyzing failures. Deleting it prematurely can lead to wasted time and the potential need for rerunning lengthy tests.
Use data restoration techniques to automate populating test beds with test data specific to the test case being run. Depending on the complexity, the test data can be generated on-demand or sourced from a centralized test data store for scalability and consistency. For tests that modify data and require reruns, use a caching system to quickly and cost-effectively revert the dataset, minimizing bottlenecks in the testing process. Automating test data restoration saves time and effort for teams, enabling them to focus on actual testing activities instead of manually test data management.
Continually monitor the speed, accuracy, and relevance of test bed setup and execution. As testing requirements evolve or data volume and complexity grow, make necessary adjustments. Provide immediate feedback to the development team if there is a failure arising from test bed setup, data inconsistency, or test execution.
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