The case for moving beyond traditional approaches to test data management

Rigorous testing at the speed of today’s release cycles requires on demand access to “good” data. That means data combinations with which to execute every positive and negative test scenario, available exactly when and where test teams need it. The rich data must also be prepared at the speed with which automation frameworks burn through it, and must be available on tap for each and every unique test case.

The logistics of test data

However, current TDM “best practice” often force QA teams to make the undesirable choice between the requirements of stringent data protection requirements, testing quickly, and executing tests with sufficient test coverage. The primary problem is: data masking can provide production-like data without personally identifiable information (PII), but does not enhance the coverage flow variety production copies.

Masking complex data reliable can furthermore create delays as test teams are dependent on an upstream team with centralised control of the data. Once the production subset has been masked and copied to QA environments, ‘parallel’ test teams often then also compete for the limited number of copies, and further delays arise from data conflicts during data-driven test execution.

In other words, these “logistical” approaches to TDM focus on moving masked copies of production as quickly as possible to QA environments, but this undermines both testing agility and quality. Rigorously testing at the speed of agile and DevOps instead requires a new TDM paradigm, moving beyond the logistics of “subset, mask, and clone” provisioning.

Test Data Automation: The new paradigm in test data

Test Data Automation” is an approach to test data that shifts focus away from moving existing data, instead delivering the right data combinations as tests are created and executed. It is therefore test-driven and automated, embedding test data preparation as a standard step within automated test execution.

Test Data Automation is also self-service and on demand, empowering testers to re-use test data processes to prepare the data they need. This eliminates the silos between test data, test creation, and test execution, removing upstream dependencies and ensuring that every test has unique data attached to it.

Curiosity is co-hosting a Meetup on Test Data Automation in Ultrecht on November 28th. Each week leading up to the event, we will publish a blog exploring “Test Data Automation” in terms of each of the three core test data requirements:

  1. Compliance: Next week, we’ll be asking “GDPR and testing: Are you a sceptic or a gambler?”
  2. Testing rigour: This article will consider the need to synthetically generate data not found among existing copies.
  3. Testing agility: We’ll then see how an on demand and automated approach to test data provides parallel access to the data tests need, when they need it.

I hope you enjoy the upcoming blogs, and that you can join CloseSure and Curiosity Software Ireland on November 28th!

[Image: Pixabay]