{"id":10645,"date":"2020-08-26T18:28:05","date_gmt":"2020-08-26T18:28:05","guid":{"rendered":"https:\/\/www.genrocket.com\/blog\/?p=10645"},"modified":"2026-06-10T14:17:51","modified_gmt":"2026-06-10T14:17:51","slug":"designing-dynamic-data-for-complex-workflow-testing","status":"publish","type":"post","link":"https:\/\/www.genrocket.com\/blog\/designing-dynamic-data-for-complex-workflow-testing\/","title":{"rendered":"Designing Dynamic Data for Complex Workflow Testing"},"content":{"rendered":"

Wikipedia defines dynamic data<\/u><\/a> as information that changes\u00a0asynchronously\u00a0over time as new information becomes available. In financial services, the term is synonymous with transactional data<\/em>, 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.<\/em><\/p>\n

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Let\u2019s explore the concept of dynamic data by looking at a familiar online banking workflow and then explore some non-financial workflows. The table below illustrates the state transitions for an online banking workflow.<\/p>\n

As the user interacts with the application to view account balances, deposit checks, transfer funds and withdraw cash, the account balances dynamically change. Each user action represents a state transition that requires stateful test data to validate test case assertions.<\/p>\n

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To fully test this workflow, the tester must have knowledge of its business rules and translate them into assertions. Then the tester must provision test data for both positive and negative testing scenarios. An exhaustive variety of data inputs is needed to fully test the many potential combinations of valid, edge case and invalid data scenarios.<\/p>\n

The first test data challenge is to provision the controlled variations of data needed to maximize coverage. That\u2019s difficult to do with a random data subset copied from a production database, but easy to do with GenRocket\u2019s synthetic test data generation<\/a> platform.<\/p>\n

The tougher challenge is validating the account balances that potentially change state at each stage of the workflow. To meet this workflow testing challenge, test data must be rules-based, dynamic and stateful. Let\u2019s define each of these terms more precisely.<\/p>\n

Rules-based:<\/strong> Test data must conform to business rules for minimum account balances, maximum cash withdrawals, and methods for updating account balances. GenRocket provides a self-service module, called Test Data<\/a> Rules, that allows testers to define business rules for generating real-time synthetic test data.<\/p>\n

Dynamic:<\/strong> Test data must be dynamically inserted during the workflow in response to user interactions with a variety of inputs to validate outcomes and test error handling. GenRocket generates data in real-time during test execution and uses an intelligent API to retrieve and insert data from within the testing application<\/a>.<\/p>\n

Stateful:<\/strong> Test data must follow the state transitions of the workflow. In the example above, checking and savings accounts must reflect an accurate balance at each stage of the workflow as funds are deposited, transferred and withdrawn. GenRocket<\/a> maintains stateful test data during text execution and refreshes the data for each successive test run.<\/p>\n

Here are some additional examples of financial workflows that require dynamic data:<\/p>\n