SeedFactory is a toolkit for test data generation.
The initial version was launched about a year ago. During this time, the library was tested in a real production project. And now, I’m happy to present it to the broader public
The current version is 0.5.0.
The main idea of SeedFactory is to generate data in tests according to your application business logic (read as context functions if you use Phoenix Contexts) whenever it is possible and avoid direct inserts to the database (as opposed to ex_machina).
This approach allows you to minimize testing of invalid states, as you’re not forced to keep complex database structure in your head in order to prepare test data.
The library is completely agnostic to the database toolkit.
The source code is available on GitHub. Check out Overview section in README file.
Please refer to docs for details.
This is great. I’ve tried a bunch of factory approaches, but this one feels quite well executed and seems to cover the needs I generally have quite well. I also really like that it makes it impossible to insert data in a way the codebase wouldn’t.
Actually, command resolver can use whatever code you need. So, you can use something that differs from the actual implementation, like a simplified version of it. But the fact that it is placed in a single centralized place makes it very maintainable.
When I read your comment for the first time, I was surprised why do you think so, as there is only 1 macro – produce/1. Completely forgot that DSL for schema definition is built on macros That’s because the heavy lifting is done by spark (@zachdaniel thank you for the library, support and quick bug fixing!)
I tried to describe the schema using simple structures in my initial implementation. But eventually, current implementation won.
The same trait can now be defined with different commands. This enables schemas that mirror real business logic where the same state can be reached through different paths:
command :create_user do
# ... creates user with status: :pending
produce :user
end
command :activate_user do
param :user, entity: :user, with_traits: [:pending]
# ... updates user to status: :active
update :user
end
command :create_active_user do
# ... creates user with status: :active directly
produce :user
end
# :active can be reached via transition...
trait :active, :user do
from :pending
exec :activate_user
end
# ...or directly
trait :active, :user do
exec :create_active_user
end
# Unique marker for the direct path
trait :pending_skipped, :user do
exec :create_active_user
end
Usage:
# SeedFactory picks the first declared command by default
produce(ctx, user: [:active])
# Force a specific path using a trait unique to that command
produce(ctx, user: [:active, :pending_skipped])
When multiple commands can produce the same trait, SeedFactory automatically picks one based on which commands can satisfy their dependencies without duplicating existing entities.
Compile-time validations
Schemas are now validated more thoroughly at compile time with helpful “did you mean?” suggestions in error messages.
Documentation
README now includes an “Advanced features” section covering pre_exec/pre_produce, args_match/generate_args, and trail tracking.
That’s because the heavy lifting is done by 
