The Age of Synthetic Test Data has Arrived

The use of synthetic data for software testing to add to, or replace, production data is not a new concept. However, the misperception about synthetic data being limited to randomized, non-realistic data is gradually disappearing. Many QA professionals are now integrating real-time synthetic test data generation with test automation tools to improve and accelerate the process of continuous testing and integration.

We use the term Intelligent Test Data Automation to represent the latest generation of technology that allows any tester to configure controlled and accurate test data for simulating any data environment. Whether it’s a structured relational database, unstructured Big Data, or a complex transaction data feed, GenRocket produces the precise data needed for testing any application at scale. Testers simply associate GenRocket’s intelligent data generators with the desired output data formats to provision data using a self-service solution appropriate or any level of functional, non-functional or regression testing.

There are many advantages for using synthetic data to replace or augment production data during the software testing lifecycle. We’ve summarized 6 essential test data criteria that we recommend to QA professionals when evaluating solutions for their test data challenges. Just download our technology brief to learn about, and apply them, to your situation.

Test Data Design Should be Integral to Agile and DevOps

The test data needed for delivering quality at the speed is often the monkey wrench thrown into a well-oiled Agile or DevOps machine. Provisioning and refreshing production data adds days to the process. As a result, many QA teams have resorted to manually creating some or all of the test data they need in spreadsheets. The problem with spreadsheet data is its inability to enable a full suite of positive, negative, edge case and combinatorial test procedures, in volume and with referential integrity intact.

With GenRocket’s Test Data Automation platform, test data can be designed along with each Agile test case as a unified process. Click on the link below to learn how GenRocket fits perfectly in an Agile or DevOps environment to enable continuous testing and integration with a self-service platform for Test Data Automation.

Test Data Solutions for Banking and Financial Services

GenRocket’s approach to Test Data Automation has been deployed across a wide variety of industry segments and in large enterprise environments around the world. One area in particular where GenRocket can provide extremely versatile and valuable synthetic test data solutions is banking and financial services.

With GenRocket, QA teams have ability to import any data model, simulate complex data feeds, replace sensitive customer account information, selectively blend production data, and apply rules and conditional logic for validating complex transaction workflows with the use of dynamic test data.

The chart below illustrates the breadth of applications and use cases that can be supported by the GenRocket platform. They range from testing payment data feeds and credit card transactions to validating ETL applications for data lakes and data warehouse environments. GenRocket can scale to generate billions of rows of test data to enable soak and performance testing. And GenRocket integrates with salesforce and other packaged applications to provide a seamless testing environment. If you don’t see a feature or capability that matches your specific needs, just ask us. We have the ability to tailor the platform.