Synthetic Test Data Generation Using Generative Models

Abstract

The market needs on high-quality, privacy-compliant and scalable test data has grown exponentially as AI-based applications and the software testing needs have grown. Limits Common to Traditional Data Collection. Traditional data collection techniques have weaknesses associated with privacy issues, inadequate coverage of edge cases, and high costs of effort. A new solution to these challenges synthetic data generation via generative models  has become a viable option. The aim of the paper is to investigate how recent advances in generative models, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models, can be used to create synthetic test datasets that have statistical fidelity while also ensuring user privacy. Explain what architectural elements, training, and validation techniques were employed in building such models, with special consideration of maintaining data diversity and realism. The experimental findings indicate that modern generative models are capable of producing synthetic data that closely resembles the real-world distribution and can be used to substantially increase software test coverage, especially in covering edge cases and areas where compliance is relevant, such as finance and healthcare. Moreover, the combination of the differential privacy mechanisms proves the possibility of regulated and secure synthetic data pipelines. This paper highlights the advantages, challenges, and potential applications of generative models in synthetic data generation. These findings suggest that hybrid methods, which combine both synthetic and minimally obfuscated real data, are the most effective approach to strike a balance between realism, privacy, and practical usefulness in real-world testing situations