Test Data Management Evolves into Test Data Automation
The 2019-20 edition of The World Quality Report, identified two of the top QA management objectives as detecting software defects before going-live and implementing quality and security checks earlier in the development lifecycle.
Clearly, the desire to shift-left is a primary concern for most QA organizations. According to a second industry report on The State of Agile, 97% of organizations now practice Agile methodologies and 74% of respondents cite accelerating software delivery as the top reason.
Agile has redefined software testing from the traditional find and fix bugs approach to the more proactive prevent bugs approach. Through the deployment of test automation, QA is becoming an integral part of a continuous integration and testing process.
Increasingly, Test Data Automation is being incorporated into Agile testing to fully realize the efficiencies of automation and to enable a continuous testing process that is integrated into the software release pipeline.
The 2019 edition of the Continuous Testing Report (Capgemini and Sogeti), found that 36% of respondents spend more than 50% of their testing time searching, managing, maintaining and generating test data. When asked about automation strategies they plan to pursue during the coming year, 35% of respondents (the highest ranking) identified Test Data Automation.
Enterprise Test Data Automation
GenRocket, the leader in Real-Time Test Data Generation technology, has identified Enterprise Test Data Automation as the next generation of Test Data Management. When fully deployed, it delivers a comprehensive solution for industry-wide problems with provisioning quality test data in less time, at a lower cost.
Self-Service Test Data Provisioning for Continuous Testing
To be effective, Enterprise Test Data Automation must be deployed in conjunction with self-service provisioning to give testers the ability to create the data they need whenever they need it. Self-service goes directly to the problem of eliminating wait states and puts control over the volume and variety of required test data in the hands of the tester.
QA organizations that follow Agile best practices often classify their test procedures as Unit Testing, Service (API) Testing, and UI (end-to-end) Testing to represent various types of functional, integration, security, compliance and performance testing. As test procedures are developed, they should map to corresponding test data requirements for achieving test goals.
GenRocket has implemented a series of self-service modules for its Enterprise Test Data Automation solution called GSelf-Service. Each self-service module provides a different capability for creating high quality test data on-demand for any type of testing.
Test Data Rules allows testers to apply if-then relationships to the nature of the test data that is needed to test and validate the business logic used by the code to be tested.
Test Data Queries allows testers to blend queried production data with real-time synthetic test data to combine the control that made possible by generating synthetic data with the accuracy associated with real production data.
Test Data Cases provides an intuitive way for testers to integrate multiple Test Data Scenarios into logical groupings that map to a test plan for a given application under test.
To learn more about the new capabilities available in GSelf-Service, visit our website for a complete description with additional links to more technical information in our knowledgebase.
Blending Synthetic Test Data with Queried Production Data
There are times when solving a test data challenge requires testers to combine highly controlled patterns of synthetic test data with queried production data to achieve the best of both worlds – test data variety and accuracy. This is achievable on a small scale by creating a production data subset, manually modifying the data to achieve test goals and then masking it to ensure data privacy.
However, this can be an extremely difficult challenge when a large variety of data value combinations must be tested to fully validate the code, or when performance and load testing requires an extremely high volume of data.
GenRocket has created a new capability that allows testers to solve this difficult challenge. Through the use of its Test Data Queries self-service module, testers and developers can perform production data queries, blend the retrieved data with real-time synthetic test data and output the combined data in any desired format for testing. The GenRocket runtime engine provisions blended test data in real-time and on-demand.
To learn more about GenRocket’s ability to blend synthetic test data with queried production data, watch a video demonstration of this powerful capability.