In the previous article “Driving excellence with Test Data Management: overcoming challenges and utilizing data demands in SAP” we delved deeper into the challenges faced in Test Data Management (TDM) and explored the various data demands scenarios.
But what does it mean practically for test automation?
In this article, we will explore the three key approaches to test data management: creating fresh data, leveraging existing data pools, and reverting processes to restore data to a usable state. We will also delve into the realm of test automation, examining how test data is generated and manipulated to support efficient and reliable testing. Additionally, we will discuss the challenges associated with data identification and provision, along with the importance of establishing a reliable data pool for comprehensive testing.
At a high level, there are typically three approaches to handling test data:
- Creating data afresh, which can include both transactional data and master data.
- Identifying and utilizing data from an existing data pool, such as migrated data loads.
- Closing or reverting a process under test to restore the data to a usable state.
In test automation, creating test data is generally straightforward. One option is to develop additional automated tests that generate data prerequisites or mock data through manual postings or IDocs. This approach works well for end-to-end automation when transactional data is generated during test execution itself.
A more sophisticated approach involves logically grouping tests in a way that helps create test data for one another. For example, executing procurement scenarios before sales-related tests.
Data identification and provision from an existing data pool can be challenging. This recurring activity can be performed in several ways:
- preparation of additional automated scripts for data identification and execution before the main automated test scenario. This requires a detailed understanding of the data attributes important for the test and should be integrated and tailored to the test flow,
- manual preparation of the test data set and refreshing test sheets, test data containers, or the test database in the automation tool can be done.
Obtaining an existing data pool can be achieved by utilizing data from the data migration team, which is typically planned once or twice a year or performed as trial data migrations for new countries integrated into the SAP template. Another option is data virtualization or synthetic data generation, which is supported by various tools available in the market. The core idea of this method is to prepare a representative data subset or a golden copy, map it to the automated tests, and reset it after each test run or within a certain period. This copy should not remain static and requires regular review or a fresh production copy to stay up to date.
The last approach we highlight here pertains to the functional options within a system under test. Automated tests can be sequenced or prepared in a way that logically completes or reverts the process under test, thereby restoring the test data to a usable state. For instance, changes made to a contract can be submitted and approved in the system, or conversely, rejected and removed from the contract. In both cases, the contract becomes modifiable again and can be reused for subsequent tests.
Conclusion
Effective test data management is essential for achieving successful software development outcomes. By implementing the three key approaches of creating fresh data, leveraging existing data pools, and reverting processes, organizations can ensure accurate and reliable testing. Test automation further enhances efficiency in generating and manipulating test data, streamlining the testing process.
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