Testing Complex Workflows with Dynamic Synthetic Data

by admin on Nov 04, 2020

When testing systems that process transactions or use business logic as part of an end-to-end workflow, QA teams often provision test data from a production data source as opposed to generating synthetic data. The reason is the need for realistic, dynamic data to reflect the many state transitions that impact test data during an application workflow.

For example, online banking systems must accurately transfer money and update balances across multiple accounts according to business logic for ensuring the proper flow of funds. Test data must accurately reflect each state transition when testing this dynamic process.

Synthetic data is not often used for testing complex workflows because it’s assumed to be random, static, artificial data and simply not up to meeting this test data challenge. However, GenRocket is fully able to meet and exceed this challenge with its Test Data Automation platform. GenRocket’s use of intelligent automation allows testers to design dynamic, rules-based and stateful test data in controlled patterns and permutations. This allows testers to rapidly provision dynamic data that is more comprehensive and secure than queried and masked production data.

How to Use Synthetic Data to Maximize Test Coverage

Synthetic test data generation is experiencing rapid adoption in Agile and DevOps environments because of the need for speed and the assured security of synthetic data. However, one of the most important benefits of GenRocket’s Test Data Automation solution is the ability for testers to maximize test coverage and reduce the number of defects that escape to production systems.

Properly designed synthetic test data can increase coverage from under 50% to over 90% when provisioned by an advanced Test Data Automation system.

With GenRocket, testers learn to think differently about test data. The realization that test data can be DESIGNED during an Agile sprint, integrated with an automated test case and generated on-demand during test execution becomes an Aha Moment.

In this article, we present the best practices for designing test data that meets the full scope of any test plan and maximizes test coverage by following the GenRocket Methodology. It’s a proven approach for modeling, designing, deploying and managing synthetic test data generation through the use of Test Data Automation.

Self-Service Provisioning for X12 EDI Synthetic Test Data

As health insurers continue to automate the processing of insurance claims, they face significant financial and security risks. That’s because an estimated 80% of claims have incorrect or incomplete data. This substantially reduces efficiency, but more importantly, it increases the potential for overpayment of claims, or subjects them to fines and penalties for late payments. Additionally, medical fraud and abuse is on the rise, representing 3% to 10% of all healthcare costs. Medical fraud is fueled by data breaches and exposes insurers to HIPAA violations, lawsuits and loss of customer trust.

To reduce the risk, GenRocket provides an X12 EDI Test Data Automation solution that generates comprehensive X12 EDI transaction data on-demand. Its latest release accelerates the test data provisioning process with powerful self-service modules and ready-to-use transaction set configurations.

Using the GenRocket platform, any tester can quickly provision clinically accurate and controlled test data for any category of automated testing, enabling QA teams to:

      Improve the accuracy of submitted claims and payments
      Ensure the privacy of patient data with synthetic data
      Fully automate the healthcare administrative workflow

Learn more about GenRocket’s unique approach in a comprehensive EDI Test Data Automation Solution Guide. It describes new capabilities that accelerate the provisioning of EDI test data with ready-to-run EDI Transaction Examples that can be quickly and easily tailored for any EDI implementation.

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See how GenRocket can solve your toughest test data challenge with quality synthetic data by-design and on-demand