GenRocket Blog

A global financial services company follows an Agile development process to continuously update their core applications. They have established a continuous delivery pipeline for releasing new features into production and they are leveraging test automation tools to accelerate the cycle time for each release. They also established a rigorous regression testing framework to ensure software defects are caught before they are introduced into the production environment. Fixing bugs in production is time-consuming, expensive and negatively impacts the digital customer experience.

QA professionals know the importance of full test coverage for catching hidden software defects. The impact of undetected bugs that leak into production can range from inconsequential to nothing short of catastrophic. A recent Internet outage provides a real-world example of how a single bug can disrupt the global operation of digital business on the Internet.

As the technology leader in synthetic test data generation, GenRocket has created a new paradigm for Test Data Management (TDM) to meet the need for speed when testing in Agile and DevOps environments. Traditional TDM requires modernization. Copying, masking and subsetting production data is simply too time-consuming and cumbersome for the age of Agile.

Wikipedia defines dynamic data as information that changes asynchronously over time as new information becomes available. In financial services, the term is synonymous with transactional data, but the concept of dynamic data is not unique to financial transactions. Dynamic data can result from any change in state during the execution of an application workflow. To properly test workflows with dynamic state changes, the data used for testing must also be dynamic.

There is growing interest among quality assurance professionals in the use of synthetic data generation for software testing. Their interest in synthetic data is usually triggered by a requirement for data privacy or the need for accelerated data provisioning for Agile and DevOps. However, the most compelling reason to augment the use of production data, or manually created spreadsheet data, with synthetic data is to have total control over the variety of data needed to maximize test coverage.