Make use of synthetic data
Requirements engineering
Describing complex data structures accurately in requirements is hard. With synthetic data constellations, requirements can easily be enriched by this aspect. This fosters mutual understanding between business and IT and lays the foundation for later test data.
Automated tests in pipelines
Automated tests need a stable baseline to provide deterministic results. Maintaining this manually directly in the test code is no fun. However, generating and consuming realistic data constellations with iSynth directly in the pipeline is!
Smoke tests
Before new software builds are staged into higher-level test environments, their baseline quality must be verified. This is where the puzzle pieces come together. Automated smoke tests conducted with the same data set already used for development ensure just that.
Coding
Coding without data is like cooking without spices. With iSynth, it's easy to provide specification-based synthetic data directly to development, whether it's done in-house, by an external partner, or off-shore.
Load and performance testing and tuning
Load and performance testing in the cloud requires large data volumes with sufficient variance. Preferably synthetically produced! The same applies to performance tuning, where developers need to have large data volumes directly at hand.
System integration testing
Many companies still rely on manual verification of software quality before a release is rolled out. The combination with automated tests enables a gradual transition to a consequent continuous delivery approach. Enriching real data with synthetic data facilitates this transition.
Bug reports
Bug reports based on real data are problematic. Synthetic data is CID free by design and bug reports do not require redaction or a DLP in place. In addition, the same synthetic data constellation that was used to detect a bug can be provided to development. This speeds up debugging and fixing.
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