Large Test Case Data Files Solution

## Background
Our test data files fall into two categories:
1. **Regular Files (<100MB)**: Stay in git repository
2. **Large Files (>100MB)**: Need alternative storage solution

Example large file from `testdata.webkit.json`:
```json
{
"josh_ios15_ffs": {
"artifacts": {
"webkitCacheRecords": {
"file_count": 4137,
// Creates a 300MB zip file - too large for regular git
}
}
}
}
```

## Storage Options for Large Files (>100MB)

### 1. GitHub LFS
- **Cost**: Free tier - 1GB storage/bandwidth per month
- **Pros**:
- Native git integration
- Automatic with GitHub Actions
- Version controlled
- **Cons**:
- Limited free tier
- Additional bandwidth/storage = $5/50GB/month

### 2. Azure Blob Storage
- **Cost**: ~$0.25/month for 25GB
- **Pros**:
- Cost-effective
- Reliable
- Easy GitHub Actions integration
- Team maintains control
- **Cons**:
- Not free
- Requires setup/maintenance

### 3. Google Drive
- **Cost**: Free tier - 15GB
- **Pros**:
- Free storage
- Familiar interface
- **Cons**:
- API setup required
- More complex GitHub Actions integration
- Manual version control

### 4. GitHub Releases
- **Cost**: Free
- **Pros**:
- 2GB file limit
- No bandwidth limits
- Native to GitHub
- **Cons**:
- 10GB total limit
- Manual upload process
- Less automated

## Questions for Discussion
1. Is cost or automation more important?
2. Do we anticipate many large test files?
3. How important is version control for test data?
4. Who will maintain the storage solution?
5. Are there other cloud services to consider?

Das Issue behandelt Speicherlösungen für große Testdateien (>100MB) wie jene, auf die in testdata.webkit.json verwiesen wird. Die Recherche umfasst die Bewertung von GitHub LFS, Azure Blob Storage, Google Drive und GitHub Releases hinsichtlich Kosten, Automatisierung und Integration in GitHub Actions. Die Arbeit erfordert ein Verständnis der Struktur der Testdaten des Projekts und die Implementierung einer ausgewählten Lösung, die wahrscheinlich Konfiguration und Skripting umfasst.

Vom Indexierungsmodell aus dem Issue-Text verfasst.