Test Data Automation
Test Data Management in DevOps
Challenges
Applications and databases have their own logic and unique properties. For test data to be useful in these contexts, it must reflect production characteristics such as:
- selection conditions (business rules)
- column attributes and transformations
- inter-field/key relationships (referential integrity)
- value ranges and inter-column calculations
Production-quality test data must also have these attributes:
- type - correct column / field values and formats
- width - values with current (and future) ranges
- frequency - realistic value occurrence patterns
- depth - volumes that address scalability concerns
In a fast-paced DevOps and Continuous Integration (CI) / Continuous Deployment (CD) environments, the ability to generate and automate consistent, and realistic test sets that can range widely in format and volume can be a tall order, distracting programmers with tight delivery timelines.
Solutions
Applications developed with realistic data formats and volumes are more likely to succeed in production. The IRI RowGen test data creation package uses production metadata to synthesize custom test sets with randomly-generated data, and/or randomly selected data from production sources. IRI FieldShield and IRI DarkShield data masking software can also be used to find and mask sensitive data in production and move bespoke targets into lower environments for testing.
To generate the right values and value ranges, RowGen uses conditional selection and formatting parameters. RowGen further enhances test data realism through referential integrity, frequency distributions, and built-in transformation and formatting functions. For example, you can randomly select data and specify ranges from pools of real data and weighted numbers (respectively).