Testing Powered by AI/ML Synthetic Test Data: A Game Changer

The session covers a testing approach that utilizes a new age Test Data Management (TDM) technique which learns from production data. Machine learning analyzes the data to create a model capable of generating synthetic data with identical source data attributes. Multiple learning iterations refine the model, enhancing its accuracy with each cycle. The data model incorporates security measures like differential privacy, enabling safe movement to lower environments. Later, generative AI leverages this model to produce desired volumes of test data for various testing types, including functional, regression, and performance testing. This approach offers significant advantages over traditional methods by eliminating the need for manual data masking and PII/PHI identification. It ensures production data ratios and streamlines the overall process, making it faster and more efficient. By enabling synthetic data-driven testing, teams can shift testing efforts left in the development cycle. This automated approach empowers teams to focus on reviewing, improving, and expanding the solutions, as the tools handle a majority of the work. To learn more about the approach outlined, join Praveen in this session.