We are happy and proud to announce next meetup!
This time João Paulo Gomes will deep dive into the advantages of isolating tests within ephemeral environments to enhance reproducibility and consistency in Spring Boot projects and Elias Nogueira will talk about 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.
The meetup will be hosted by JDriven.
Please join us and RSVP!
Because of the limited number of seats, please keep your RSVP up-to-date, so we can welcome someone else of you can't make it.
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17:30 Doors open
18:00 Food & Drinks
19:00 Ephemeral environments for your Spring Boot E2E tests, by João Paulo Gomes
20:00 Break
20:15 Datafaker: the most powerful fake data generator library by Elias Nogueira
21:15 Drinks
Giveaways
1 JetBrains licence
Talks
Ephemeral environments for your Spring Boot E2E tests
In this talk, JP will deep dive into the advantages of isolating tests within ephemeral environments to enhance reproducibility and consistency in Spring Boot projects. He will be using tools such as Docker, Docker Compose, and TestContainers. You can run these tests either locally or seamlessly integrate them into your CI pipeline.
Datafaker: the most powerful fake data generator library
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
Speakers
João Paulo Gomes
João Paulo Gomes, or JP, is a Software Engineer from São Paulo (Brazil). He relocated to the Netherlands in 2022. He has been working as a Software Engineer for 16+ years. Most of the time in the finance industry. Next to coding, JP loves to cook, read, write, and spend time with family and friends.
Elias Nogueira
Elias is a Senior Principal Engineer at Backbase, Java Champion, Oracle ACE for Java, Java Magazine NL editor, TDC Rockstar, and Browserstack Champion.
He helps software engineers to develop their quality mindset and deliver bug-free software.
