Files
plezy/CONTRIBUTING.md
edde746 bb3762ed63 ci: remove the Android Maestro e2e workflow
The Maestro suites remain runnable locally through scripts/run_maestro.py
and scripts/run_maestro_ci.py; drop the workflow, the test that parsed it,
and the CONTRIBUTING reference to automatic PR coverage.
2026-08-08 12:36:49 +02:00

144 lines
6.5 KiB
Markdown

# Contributing
## Getting Started
1. Fork and clone the repository
2. Run `flutter pub get` to install dependencies
3. Run `scripts/codegen.sh` to generate translations and Dart model code
4. Start developing!
## Development
- Follow Dart/Flutter conventions
- Run `dart format .` to format Dart code (note: generated files like `*.g.dart` are excluded from CI checks)
- Run `scripts/format_native.sh --fix` to format Kotlin, Swift, C++, C, Objective-C, and native headers
- Run `flutter analyze` before submitting to check for issues
- Run `scripts/run_tests.sh` to run the test suite (same as `flutter test`, but scaled to your core count)
- Test your changes thoroughly
### Code Quality Checks
The project includes automated CI checks that run on all pull requests:
1. **Code Formatting**: Ensures code follows Dart and native formatting standards
- Run locally: `dart format .` to format Dart files
- Run locally: `scripts/format_native.sh --fix` to format native files
- Note: CI only checks non-generated files (excludes `.g.dart`, `.freezed.dart`)
- Generated files are reformatted automatically by build tools
2. **Static Analysis**: Checks for code issues and potential bugs
- Run locally: `flutter analyze`
- Note: CI excludes generated files from analysis (configured in `analysis_options.yaml`)
3. **Generated Code**: Ensures generated translations and model files are current
- Run locally: `scripts/codegen.sh --check`
4. **Tests**: Runs unit and widget tests (when available)
- Run locally: `scripts/run_tests.sh`
- This is `flutter test` with `-j` set to the core count. The default is half your cores, which
leaves most of the machine idle because the suite is dominated by per-file compilation.
Arguments are forwarded, so `scripts/run_tests.sh test/widgets/some_test.dart` works.
All these checks must pass before your changes can be merged.
### Maestro end-to-end tests
Android E2E tests use [Maestro](https://maestro.mobile.dev/) against a disposable, pre-seeded Jellyfin container.
Prerequisites: Java 17, Flutter and Android SDK/platform tools, a running Android emulator, Docker, and the
[Maestro CLI](https://docs.maestro.dev/getting-started/installing-maestro).
Run the suites from the repository root (`py -3` can replace `python3` on Windows):
```bash
python3 scripts/run_maestro.py basic # Basic user flows
python3 scripts/run_maestro.py catalog # Catalog and music flows
python3 scripts/run_maestro.py media # Codec playback and track selection
```
Run one flow with `--flow`:
```bash
python3 scripts/run_maestro.py basic --flow .maestro/flows/04_search.yaml
```
Use `--skip-build` to reuse the debug APK and `--skip-jellyfin-build` to reuse the Jellyfin image. Set
`--device <adb-serial>` when multiple devices are connected; physical devices also require `--adb-reverse`.
Top-level flows live in `.maestro/flows/`, shared setup in `.maestro/subflows/`, and focused regressions in
`.maestro/regression_flows/`. Automatic groups are declared in `scripts/run_maestro_ci.py::GROUPS`. Every top-level
regression flow must be registered either there or in `DESTRUCTIVE_MANUAL_TARGETS`; reusable subflows are not
independent tests. A manual-only classification must state why the flow cannot run automatically.
The profile-isolation and profile-teardown regressions create and remove profile connections, so they are a destructive
manual target rather than an automatic group. Run them only against the pre-seeded Jellyfin fixture and a disposable
emulator, using the required opt-in:
```bash
python3 scripts/run_maestro_ci.py profile-regressions --disposable-emulator
```
The target refuses to start without `--disposable-emulator`. Each profile flow writes to its own Jellyfin log and
diagnostics directory under `build/maestro-profile-regressions/`.
### Production container image updates
Production images in `server/Dockerfile` and `server/docker-compose.yml` use a readable version or source-revision tag
plus an authoritative multi-platform index digest. The adjacent `Platforms` declaration records the supported
`linux/amd64` and `linux/arm64` variants. Never replace these references with a mutable tag or a single-platform child
manifest.
Update a production image only through a reviewed change:
1. For the Bugs service, first record the running container's image ID, repository digest, platform, and OCI source
revision without printing its environment. Prefer that reviewed running identity; selecting anything else is a
service upgrade, not a routine pin refresh.
2. Review the upstream source revision and changelog, provenance, vulnerability results, and manifest contents. Resolve
the readable tag and digest-qualified reference independently and confirm they identify the same OCI index in two
clean caches. The index must contain both declared platforms; provenance/attestation descriptors do not count as
runnable platforms.
3. Change the readable tag, full `sha256` index digest, and adjacent platform declaration together. Include the old and
new identities, manifest/platform evidence, review findings, smoke results, and rollback notes in the change.
4. Before changing the Bugs digest, exercise it with non-production configuration and a disposable volume. Review
migrations, take a restorable `bugs_data` backup, then validate a cloned volume. A forward-only migration rolls back
with the prior digest and pre-change backup, not by changing the image reference alone.
5. Run `python3 scripts/check_container_image_pins.py`, `python3 scripts/test_check_container_image_pins.py`, and
`(cd server && go test ./...)`. Inspect the rendered Compose configuration and rebuilt images locally without
exposing configuration values. Do not publish or deploy from a review checkout, and never fall back to `latest` when
a digest is unavailable.
## Internationalization (i18n)
This project uses `slang` for internationalization with JSON files.
### Adding New Strings
1. Add your string to `lib/i18n/strings.i18n.json`:
```json
{
"section": {
"myNewString": "My new text"
}
}
```
2. Run `dart run slang` to regenerate translation files
3. Use in your code:
```dart
Text(t.section.myNewString)
```
### Adding New Languages
1. Create new JSON file: `lib/i18n/[locale].i18n.json`
2. Copy structure from `en.i18n.json` and translate values
3. Run `dart run slang` to regenerate files
### Guidelines
- Organize strings logically in nested objects
- Use camelCase for keys
- Keep strings concise and clear
- Always run `dart run slang` after changes