- How do I generate all combinations of test inputs for Laravel Dusk browser tests using this package?
- Use the `VariationsTreeBuilder` to define your test parameters (e.g., enums for UI states, ranges for numeric inputs) in a config array, then iterate the results in your Dusk tests. For example, generate combinations of login credentials or form inputs dynamically instead of hardcoding. The output arrays align perfectly with Dusk’s `browse()` method parameters.
- Can I use this package to create synthetic datasets for Laravel database seeding instead of Faker?
- No, this package is designed for in-memory combinatorial logic, not database persistence. It’s ideal for generating test inputs or feature flag variations, but for seeding databases, use Laravel’s built-in `Factory` or `Seeder` classes. However, you could combine it with Faker by using callbacks to fetch dynamic values from your models.
- What Laravel versions does coshi/variator support, and will it work with PHP 8.1?
- The package has no Laravel-specific dependencies, so it works with any Laravel version. However, it was last updated in 2016 and may require adjustments for PHP 8.1 due to untyped code and potential deprecations. Test thoroughly or consider forking the repo to add PHP 8.1 support if adopting long-term.
- How do I integrate this package into Laravel’s service container for reusable test data generation?
- Register `VariationFactory` and `VariationsTreeBuilder` as singletons in a service provider. Bind them to the container using `singleton()` with a closure that resolves dependencies. Then, inject the builder into your test classes or use a facade for cleaner syntax. Example: `Variator::build($config)` in your test cases.
- What are the performance implications of generating large combinatorial datasets (e.g., 100K+ variations)?
- Generating large combinations can consume significant memory and CPU, especially with nested or cross-referenced variations. Benchmark your use case before adoption, and consider limiting scope to critical test paths. For massive datasets, evaluate custom solutions or chunking the output iteratively.
- Does this package support dynamic values from Laravel models or APIs in the callback feature?
- Yes, the callback feature allows you to fetch dynamic values from anywhere, including Laravel models or external APIs. Pass a closure, static method call, or even a service container binding to resolve values on-the-fly. For example, use a callback to fetch user roles from your database during test execution.
- How can I handle errors if a callback in the config fails (e.g., API timeout or invalid data)?
- The package lacks built-in error handling for callbacks, so you’ll need to wrap them in try-catch blocks or validate inputs beforehand. For example, add a `try-catch` in your callback closure to return default values or skip invalid combinations. Document edge cases in your test suite to ensure robustness.
- Is there a way to cross-reference variations between fields (e.g., conditional values based on another field’s output)?
- Yes, the package supports cross-referencing via callbacks that accept previous field values. For example, define a callback for `fieldB` that depends on the value of `fieldA`. The README example shows a `DummyClass` method `someValues($text)` that dynamically adjusts values based on another field’s output.
- What alternatives exist for generating test data in Laravel, and when should I choose this package over them?
- Alternatives include Laravel’s `Factory` (for database seeding), `Faker` (for realistic fake data), or `nunomaduro/collision` (for unique test data). Choose this package when you need **explicit combinatorial logic** (e.g., testing all permutations of feature flags or edge cases) rather than random or database-backed data. Avoid it for ORM-heavy workflows.
- How can I test this package itself in a Laravel project to ensure it works as expected?
- Write unit tests for the `VariationsTreeBuilder` by mocking the `VariationFactory` and verifying output arrays match expected combinations. Test edge cases like empty configs, invalid types, and callback failures. Since it’s a dev dependency, focus on validating its integration with your test suite (e.g., PHPUnit or Pest) rather than production code.