GenRocket is the only Patented Synthetic Data in the world. GenRocket’s synthetic test data allows Quality Assurance teams to increase data coverage while reducing cycle time and productivity significantly.
Development and Testing are critical stages constantly under pressure to quickly create and test new code. They’re generally adapted using production data copies like a solution for test data, developing scripts, manually producing, or using spreadsheets to create data.
Many testers need to be made aware of the speedup that synthetic data platforms like GenRocket may provide for their testing procedures and how to begin using it. These platforms allow for test data generation in virtually any volume, variety, or format while maintaining referential integrity.
For a few functional tests, ensure that the data should seem realistic. But for many use cases, Unique patterns and sequential data are preferable.
1. GenRocket Solves the Demand for large amounts of data:
In short, this platform generates the amount of synthetic test data needed for extensive, enterprise-wide tests. It also allows you to manually furnish such data for a large volume of transactional data.
You may quickly produce past, present, or future transactions with significant volume data creation, which is suitable for enterprise-wide testing of ‘big data’ projects when more data is required than is accessible in production copies.
2. Coverage Issues in Tests:
If you have negative and edge case testing data and your Dev and test teams are having problems with large volumes, GenRocket helps you to increase the coverage. Unfortunately, only 2% to 3% of test data sourced from production offers edge case values, so manually created data is often required for such testing. This proves that synthetic data is frequently necessary for such testing without many risks.
Although production copies of data frequently lack information adhering to extreme rules and constraints which reflect some aspects of that environment. The data in these situations are produced manually or using spreadsheets. Automation of synthetic test data can significantly hasten data provisioning at this point.
3. Well-defined Data Models:
This platform can ingest several data model types, such as Database schema files, DDLs, or JSON metadata, and utilise them to quickly generate new synthetic test data. Furthermore, this platform leverages clever automation to get the information out of Data Warehouse and assign suitable data generators.
Once you’ve configured the test data case specifications and verified the data with intelligent wizards, the system generates the quantity and diversity of synthetic data required for testing.
This platform will automatically replace test data with synthetic data at runtime, ensuring that testers are not delving into the same ‘data well’ for each test run. In addition, GenRocket makes it simple to incorporate existing data.
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