Test Data Management Using Synthetic Data Generation Techniques
Keywords:
Synthetic Data, Test Data Management, Software Testing, Data Privacy, Machine Learning, GDPR, Test Automation, Generative Models, Data GovernanceAbstract
As software systems grow more complex and data-driven, Test Data Management (TDM) has become increasingly central to maintaining quality assurance, meeting regulatory requirements, and supporting realistic performance testing. Traditional TDM approaches such as masking production data or generating test data manually are proving insufficient under the weight of modern demands, especially in light of stringent privacy regulations like GDPR and HIPAA. This study explores the use of synthetic data generation as a scalable, privacy-preserving alternative for TDM. Drawing on techniques from statistical modeling, rule-based synthesis, and generative machine learning, we evaluate the ability of synthetic data to emulate production-like conditions without compromising sensitive information. Several data generation strategies are assessed across varied testing environments to examine their impact on test coverage, compliance, and overall software quality. The experimental findings suggest that synthetic data can improve both efficiency and security in testing workflows while minimizing legal and operational risks. The paper concludes with practical recommendations for integrating synthetic data practices into enterprise-scale TDM pipelines, highlighting considerations for governance, automation, and long-term maintainability.
DOI: https://doi.org/10.17762/ijisae.v12i23s.7956
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