A significant challenge lies in ChatGPT's ability to comprehend the often undocumented and complex business logic inherent in legacy systems, leading to potential inaccuracies in generated data. It often struggles with inferring proprietary data schemas and intricate relationships without explicit definitions, making it difficult to produce structurally valid test sets. Ensuring data integrity and consistency across various interconnected tables, adhering to specific legacy constraints like unique keys or custom data types, presents another formidable hurdle. Generating realistic "edge case" and "failure scenario" test data that accurately reflects the unique quirks and vulnerabilities of an old system is particularly problematic. Furthermore, the need to handle sensitive information securely while generating data, avoiding the accidental creation of PII that could violate compliance, adds another layer of complexity. The validation of generated data against undocumented legacy rules also becomes a non-trivial task, requiring extensive manual review. More details: https://sso.yongpyong.co.kr/isignplus/api/checkSession.jsp?returnURL=https://abcname.com.ua/