Perfect Test Data

Don't lose time finding and making test data. With on-the-fly lookups, refreshes, and integrated data generation, parallel tests, teams, and frameworks self-provision the data they need. Test Data Automation provides rich data-as-a-service, enabling the rapid and rigorous testing needed for continuous software delivery.

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Test Data Bottlenecks Holding Development Back?

Today, far too much time is wasted in the SDLC finding and making data. Testers and developers often hunt manually for integrated data, making data in databases, files, messages or via the front-end. The data must align perfectly across systems, but often tests fail due to unstructured lookups. Data further leaves systems exposed to costly bugs, as it lacks combinations needed in testing, while testers don’t have time to create complex data by hand. Further time is lost as tests compete for data in shared environments, using up each other’s data as “parallel” teams wait for data to become available. These bottlenecks usually grow with the adoption of test automation and CI/CD. A structured, automated approach is needed to create environments filled with rich data on demand, finding the combinations needed in testing on-the-fly.

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All the data testing, development, and CI/CD needs – available on-the-fly

Test Data Automation removes the time lost finding, making or waiting for test data, enabling automated “find and makes” that occur on-the-fly during testing, development and CI/CD. Testers and developers can submit on demand data requests using self-service forms created in intuitive business language, receiving the exact data combinations their tests need as-a-service. Each query can be made reusable for manual and automated testing, seamlessly maintaining a shopping cart of standard data “finds”. Running this catalogue of automated queries at the start of a sprint or data refresh automatically populates parallel environments with rich test data, providing bottleneck-free data to accelerate testing and development.

Automated Test Data Find and Makes-1

With Test Data Automation, testers, developers, and CI/CD pipelines access all the data they need to find bugs earlier and at less cost to fix. Integrated data “makes” fill any gaps needed to satisfy queries as they are submitted or run from the central catalogue, using query parsing, cloning and synthetic data generation to create all the combinations needed in testing. Test Data Allocation further avoids time-consuming data clashes, locking data combinations for each test to ensure that teams and frameworks work seamlessly in parallel. Integrating the automated “find and makes” with test automation frameworks and CI/CD pipelines lets tests self-provision the data they need on-the-fly, rapidly running the rigorous and targeted tests needed for continuous software delivery.

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Accelerate your testing and development

Watch these examples of Test Data Automation for SQL Server and Oracle Databases, to discover how:

  1. Manual and unstructured approaches to data lookups compare to structured and automated “find and makes”, in terms of speed, consistency, and predictability.

  2. A test data mart produces central and aggregated views of data at its technical level, enabling automated and on-the-fly data lookups for both manual and automated testing.

  3. Creating business views on top of the test data mart lets testers and developers search for data using business language, providing self-service data without bottlenecks or constraints.

  4. End users can request data by casting queries or filling in configurable forms, using menus, fields, and checklists to receive the data they need, when and where they need it.

  5. Queries and requests for new data automatically add attributes to the data mart, maintaining complete data sets to drive rapid and rigorous testing.

  6. Testers and developers can store reusable queries as they fill in self-service search forms, building a standardised library that auto-populates environments with rich test data.

  7. Test Data Allocation enables parallelised testing and development, locking data to ensure that no team, test, or framework transacts against data needed by another.

  8. Automated data “makes” create missing data automatically as data requests are made, enabling the test coverage needed to find bugs earlier and at less cost to fix.

  9. If there are insufficient rows of data to satisfy a query, SQL parsing automatically creates the missing rows on-the-fly, generating functions to find similar data to satisfy the query.

  10. Automated data cloning produces missing rows needed for rigorous testing, “crawling” across tables to create consistent data that satisfies all the relationships needed in testing.

  11. Synthetic test data generation creates data combinations needed in testing and development, using intuitive configuration and visual flows to generate rich test data.

  12. The integrated and on-the-fly data creation seamlessly inputs new data as developers issue data lookups, populating all the data needed via message queues, files, and APIs.

  13. Linking a standard catalogue of data “finds” to “makes” automatically creates the data needed at the start of a sprint, creating missing data attributes to satisfy every query.

  14. The integrated find and makes fill environments with rich data-as-a-service, running the catalogue of standard queries to populate traditional instances, containers, and virtual databases.

  15. “Exploding” data based on the catalogue of queries made by testers and developers automatically creates a varied set of rich test data for rapid and rigorous testing.

  16. Model-based data design creates intuitive flowcharts automatically from the queries made by testers and developers, using generation algorithms to create the data needed in testing.


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