Data for Application Testing
Stress Programs with the Right Ranges and Values
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 have 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 or continuous delivery environment, the ability to generate realistic test sets that can range widely in format and volume can be a tall order, distracting programmers with tight delivery timelines.
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 containing randomly-generated data, and/or randomly selected data from real sets.
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).
For continuous delivery and deployment environments, RowGen can synthesize test data at any step in a development process without depending on data being made available from another step. Furthermore, embedded data transformation and validation functionality facilitate incremental application tests that assure backwards compatibility and forward compliance with each production release.
Combine random generation and set-file selection, field-level conditions and manipulations, and custom layout features. Rapidly build the intelligent data you need to stress-test and vet your applications. Improve the quality and reliability of your deliverables.
If you still prefer to test with data in production DBs, you can also use RowGen to quickly subset and mask it. Or, you can mask an even broader range of sources with IRI FieldShield. If you need multiple capabilities, or need to virtualize test data from a variety of static or streaming sources, check out the IRI Voracity data management platform which has it all.