Elias is a Senior Principal Software Engineer, Java Champion, Oracle ACE for Java, Browserstack champion, and JavaMagazine NL Editor.
He specializes in Quality Engineering for backend, frontend, and mobile and loves sharing knowledge through his blog, writing articles, and giving talks around the world.
Data generators in software testing play a critical role in creating realistic and diverse datasets for testing scenarios. However, they present challenges, such as ensuring data diversity, maintaining quality, facilitating validation, and ensuring long-term maintainability.
While many engineers are familiar with these challenges, they often use non-specialized tools like the RandomStringUtils class from Apache Commons or the Random class, concatenating fixed data with it. This approach lacks scalability and may not yield a valid dataset.
Thankfully we have DataFaker, a library for Java and Kotlin to generate fake data, based on generators, that can be very helpful when generating test data to fill a database, to generate data for a stress test, or to anonymize data from production services.
With practical examples, you will learn how to generate data based on:
- different or multiple locales
- different generators like address, code (books), currency, date and time, finance, internet, measurement, money, name, time, and others
- custom (data) providers
- date formats
- expressions
- transformations
- unique values
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