7/21/2023 0 Comments Synthetic data generator tool![]() However, statistically representative synthetic data is not very suitable for software testing and quality assurance. Then it can safely be used for data analytics and business intelligence use cases. This results in a secure and private synthetic version of production data without its sensitive information. The first method is often associated with the use of deep learning to model and profile a production database to produce a statistically representative synthetic data replica. Synthetic data is designed and generated dynamically to meet test case requirements.A synthetic data replica is produced by scanning and profiling a production data source.Synthetic data generation systems operate in two fundamentally different ways: It’s important to understand the differences and their impact on test data quality. ![]() However, not all synthetic data generation methods and technologies are the same. It’s an essential technology for reducing test cycle time and implementing shift-left testing strategies. Synthetic data generation is much faster than manual data creation and can produce higher data volumes for load and performance testing. What is Synthetic Data Generation? Methods of Synthetic Data Generation Manual data provisioning is labor intensive, limits the volume of data that can be produced, and doesn’t represent the multi-dimensional data relationships found in complex data environments. This results in a bottleneck with testers spending 30% to 60% of their time provisioning test data. On average, 67% of testers are using spreadsheets to manually generate their test data. Augmenting production data with synthetic data increases test coverage and can prevent costly software defects from escaping to the production environment. Synthetic data is often manually created in the form of spreadsheets and used to replace or augment production data with combinations and variations not present in the production dataset. As a result, it has become extremely popular in quality assurance organizations for companies in regulated industries like healthcare and financial services. No Personally Identifiable Information (PII) is contained in synthetic data. Unlike test data sourced from a production environment, synthetic data is private and secure. It can be used for all forms of functional and non-functional testing, populating new data environments, or training and validating machine learning algorithms for AI applications. Synthetic data is artificial data that can be created manually or generated automatically for a variety of use cases. Synthetic Data Generation What is Synthetic Data?
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