LinkedIn Learning – Synthetic Data for Software Testers
English | Tutorial | Size: 250.43 MB
When real data is scarce or when privacy is paramount, using synthetic data for testing can be the solution. By artificially generating data that mimics the statistical properties of real-world data, quality assurance steps can be performed successfully and with less risk.
In this course, learn how to create such datasets and how to leverage the data during practical testing scenarios. Get introduced to algorithms and tools that can be used to create synthetic data. Receive insights regarding how to work with time-series and unstructured data, improving your confidence with handling complex data formats. A challenge in the course facilitates hands-on practice so you can start getting comfortable with the steps involved. Instructor Mike Smith also discusses the limitations associated with synthetic data, preparing you to be able navigate constraints if they arise.
Learning Objectives:
• Identify scenarios where synthetic data could be used in testing environments
• Create a synthetic dataset that mimics real-world data
• Take precautionary measures to ensure data privacy during testing
• Set up a synthetic data pipeline for quality assurance
• Use time-series data and unstructured data during testing
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