Talks

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 resort to 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
• random enum values
• different generators like address, code (books), currency, date and time, finance, internet, measurement, money, name, time, and others
• custom (data) providers
• sequences (collections and stream)
• date formats
• expressions
• transformations
• unique values