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Fake user data generator
Fake user data generator







fake user data generator
  1. #Fake user data generator update
  2. #Fake user data generator manual

When the person doing the testing is a developer, and can easily create different states.When you’d like to test using production data, without any of the risks (and in a GDPR-friendly way).When you expand the team to include a dedicated QA person.When you want to be able to test the exact state that a production user is in, in a test environment.When the data you want to mock is quite complex.Automate this through a command, or as part of your post deployment script.Use this database as your staging/test database.Iterate over all of your records, and apply a reasonable strategy to anonymise data.Replicate your production database elsewhere.For medium to large scale applications – this is well worth the effort. This strategy leaves you with dummy data that is almost identical to your production data, except anonymised. Avoid doing this: the configuration file ended up being more complex than what it replaced, rarely worked and wasn’t used. We tried to improve on this strategy by creating a UI + configuration-driven way of seeding different states.The “DummyDataSeeder” file can quickly and easily become one very long and hard to follow class.When the application was “small”, this strategy was an extremely quick way for developers to get into a productive, working state.When it’s not uncommon to want to recreate a user’s exact state on a test environment in order to better understand the issue.When it’s complicated and/or tedious to get the application into a particular state.When there are many different states your data can be in.

#Fake user data generator update

When it’s easy for whoever is doing the QA to update the seeder (ideal for when the developers are taking on the QA responsibility).

#Fake user data generator manual

  • When it’s easy to recreate states by doing a few manual steps.
  • When the different states your data can be in is limited.
  • Automate this as part of your post deployment script.
  • Drop your database and seed using this class whenever you want to start from a fresh “state”.
  • Create a seeder that leverages your existing Model Factories.
  • I had once written an article entitled “ The Perfect Staging Environment” which explores this strategy in more detail. In this article, we will explore two strategies for generating dummy data: using a ‘Dummy Data Seeder’ or ‘Scrambling Production Data’. Making it easy for application states to be achieved with minimal “setup” (that tends to be tedious, data-entry style work) increases productivity of the whole team. This dummy data is used during development, the QA process and even for feature demos. Part of creating a successful Staging and Test environment for your application is generating dummy data as part of your deployment process.









    Fake user data generator