This week we present part 2 in a series of blog articles titled Moving from Test Automation to Intelligent Automation. This installment is devoted to the topic of data modeling and referential integrity.
It’s an important subject because maintaining the structure and validity of synthetic test data is crucial for the accurate representation of a production database. Data structure is defined by a database schema or Data Definition Language (DDL) and serves as the blueprint for generating test data on-demand.
GenRocket can replicate virtually any database or data feed and provide total control over the integrity of the data. Testers, quality engineers and developers can easily import a data model and immediately design and generate synthetic test data to meet their test case objectives. There is no requirement for copying, masking, modifying and refreshing production data.
Learn how the entire QA process can be transformed into an accelerated quality assurance environment where the speed of testing and the quality code are fully optimized.