Data for Application DevOps

 

Stress Programs with Smart, Synthetic Test Data

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 build custom, synthetic test sets with randomly-generated data, and/or randomly selected data from production sources.

To produce the right volumes 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).