Test Data & AI in Everyday Practice: What Really Works Today – And What Doesn’t

Short description

Artificial intelligence can do a lot in the test data space: less manual work, more automation, faster results. In practice, however, AI has to prove itself against a range of requirements: reproducible test bases, complex business scenarios, compliance rules, and teams with completely different needs.

This is exactly where our test data management platform comes in: AI is added where it can actually play to its strengths. In our talk, we show how proven test data processes can be combined with modern AI capabilities.

Value for the audience:
A realistic understanding of when AI clearly adds value in test data management today – and when it’s better to stick with proven approaches.
Concrete insights into a platform that uses AI for test data in a model-based, controllable, and reproducible way – rather than just generating “some” data.
Practical guidance on how to evolve from reactive data provisioning to a proactive, hybrid test data strategy – without disrupting your established processes.

Problems addressed:

• Defects and complex process chains still require the level of detail only production data can provide and why AI data can complement, but not replace, them. Reproducible regression tests, stable environments and the secure handling of personal data.
• Generate realistic synthetic test data from a business domain model (e.g. customers, contracts, transactions). How can AI help respect defined value ranges, distributions, relationships, and statistics? How do we create data that looks like real prof
• Production data as a reference, safeguarded by masking and modification, enriched with AI-generated synthetic data for new or rare constellations.

Talk language: English
Level: Advanced
Target group: Testers, Test Automation Engineers

Speaker: Christoph Stock

MEHR ERFAHREN