In the realm of software quality assurance and engineering, there exists a wide array of testing categories, each tailored to ensure different aspects of system functionality, performance, and compliance. These range from unit testing, which examines individual components, to complex integrations, performance benchmarking, and regulatory compliance checks. For each testing category, having precise and relevant test data is crucial to accurately assess system behavior and ensure quality.
In our modern, data-centric world, organizations encounter numerous challenges when testing critical financial systems and applications. Managing vast amounts of data for testing these applications while ensuring data privacy, consistency and integrity can appear daunting. However, with the right tools and strategies, these challenges can become opportunities for innovation and success.
GenRocket offers 100% secure synthetic test data for healthcare claims processing system that improves accuracy and accelerates test cycle time
The meaningful use of electronic health records (EHR) has been at the forefront of the healthcare IT conversation since the 2014 American Recovery and Reinvestment Act. As of 2019, the CDC reports that 89.9% of healthcare practices such as physicians’ offices are actively using at least some or all of an EHR management system.
The strategy of Shifting left means performing software testing earlier in the SDLC so that defects can be detected when they are faster and easier to correct. Software bugs that escape to production can cost 100 times more to resolve than early in the product lifecycle.
In traditional Test Data Management (TDM), the test data lifecycle is based on the premise that some or all data used for testing is sourced from production. A full or subsetted copy of a production database is transferred to a clean room where it is examined for personally identifiable information (PII) and masked or anonymized before being transferred to a non-production environment for testing.
Software release frequencies are continuously accelerating. The latest Capgemini/Sogeti Continuous Testing Report 2020 measured the release frequencies for 500 large enterprises in North America and Europe. The results, shown in the graphic below, indicate 61% of organizations are deploying a new build on a daily or weekly basis. Another 26% deploy code hourly, while some even deploy several times per hour.
Agile adoption is in full swing. According to the most recent State of Agile Report, it’s being adopted by 95% of organizations. That’s not to say it’s easy to accomplish an Agile transformation. In fact, 77% of organizations are still in the process of implementing its methodologies as they struggle with cultural, organizational and technological change.
Part 3 in the series: Moving from Test Automation to Intelligent Automation In part 2 of this series, Data Modeling and Referential Integrity, we explored GenRocket’s ability to reproduce the structure of any database, file format or data interface standard by importing a data model and establishing referential integrity between related data tables. This capability allows a data architect to establish a framework for testers to generate any volume or variety of test data that conforms to the structure of application data sources. In GenRocket’s component architecture, data tables are referred to as Domains while columns, are referred to as Attributes. In addition to data tables in a database, Domains can define any real-world object. Think of a Domain as you would a noun: a person, place or thing.