This white paper explores how generative AI, especially GANs, can create synthetic structured test data to maximize test coverage and address data privacy in system migrations. It emphasizes generating realistic data based on learned patterns from legacy systems, enabling effective testing of new platforms without exposing real user data. Zensar’s approach includes modeling field interactions, handling edge cases, and aligning outputs between old and new systems. Benefits include faster testing, better coverage, maintained data integrity, and compliance with privacy laws.

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