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Retrieved on: 2024-10-15 17:10:03
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Summary
The article explores synthetic data generation's role in software testing, addressing its plausibility and usefulness through machine learning techniques. It involves unsupervised learning, data management, and generative models like GANs and autoencoders for creating and evaluating realistic synthetic data.
Article found on: towardsdatascience.com
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