Dev
Fake Mobile Device Info Generator
Testing mobile-specific code paths — device-based feature flags, crash report ingestion, analytics segmentation — requires device metadata that looks like real data. Hardcoded profiles from a single device miss the variation that surfaces bugs: a field that breaks on RAM values below 6 GB, a UI that overflows on a specific screen resolution, a feature flag that behaves differently on Android 10 versus Android 13. This generator produces complete device profiles as JSON objects: brand, model name, OS and version, screen resolution, RAM (Android only), storage, battery capacity (Android only), SIM slot count (Android only), NFC flag (Android only), Face ID availability (iOS only), and a synthetic device identifier. The Platform selector scopes output to Android, iOS, or a mixed batch covering brands including Samsung, Google Pixel, OnePlus, Xiaomi, and Apple across multiple model generations. Android profiles cover OS versions 9 through 13; iOS profiles cover versions 14.8 through 17.2. Count goes up to 20 per batch. All identifiers are synthetic — no real IMEIs, no real serial numbers. Profiles are safe to commit to public repos, use in GDPR deletion tests, and import into staging analytics pipelines without touching real user data.
How to use
- Choose your options above
- Click Generate
- Copy your result
Detailed instructions
- Select your target platform from the dropdown: choose Android, iOS, or Mixed depending on which device profiles you need.
- Set the count field to the number of device profiles you want, up to your preferred batch size for seeding or testing.
- Click Generate to produce a batch of complete device metadata objects with models, OS versions, resolutions, and identifiers.
- Review the output list and copy individual records or the full batch into your test fixtures, database seed scripts, or analytics mock data.
Use Cases
- •Seeding a Postgres staging database with 50+ diverse Android and iOS device profiles
- •Testing crash report ingestion pipelines in Datadog or Sentry with realistic device metadata
- •Validating device-specific feature flags across varied OS versions and RAM tiers in Firebase Remote Config
- •Generating Cypress fixture files that cover real-world screen resolutions for responsive breakpoint tests
- •Populating demo dashboards in Mixpanel or Amplitude with believable device distribution data
Tips
- →Generate a Mixed batch of 20+ devices and filter by RAM tier to test how your app behaves on low-memory handsets versus flagships.
- →When testing analytics dashboards, generate separate Android and iOS batches to simulate realistic platform split ratios like 70/30.
- →Pair screen resolutions from the output with Chrome DevTools custom device presets to get accurate viewport rendering tests.
- →For crash report testing, regenerate until you have profiles spanning at least three major OS versions per platform to catch version-specific bugs.
- →Use the device identifiers as foreign keys in test databases to link fake sessions, events, and crash logs to consistent device records.
- →If your feature flag system targets RAM thresholds, generate a large batch and filter profiles at the exact cutoff to find boundary condition bugs.
FAQ
what fields are included in a generated mobile device profile
Android profiles include brand, model, OS (Android), OS version (9 through 13), screen resolution, RAM in GB, storage in GB, battery capacity in mAh, SIM slot count, NFC flag, and a 16-character hex device identifier. iOS profiles include brand (Apple), model, OS (iOS), OS version (14.8 through 17.2), screen resolution, storage in GB, a Face ID availability flag, and a hyphenated alphanumeric identifier. iOS profiles do not include RAM or battery fields.
can I use fake device IDs to test push notification routing
You can use them to test how your system stores, deduplicates, and routes token data, but not to send real pushes. APNs and FCM validate tokens against actual device registrations, so generated identifiers will be rejected at the delivery stage. Pair this tool with a real device or simulator for end-to-end push tests.
is it safe to commit generated device data to a public GitHub repo
Yes. All profiles are entirely synthetic — no real IMEIs, no real serial numbers, and no user linked to any identifier. There is no PII, so you can freely commit the output to public repositories, include it in documentation, or use it in destructive GDPR deletion tests without touching real user records.
how do I use this data to test a feature flag system targeting device specs
Generate a large mixed batch and filter profiles by the specific threshold your flag targets — for example, RAM below 6 GB or OS version below Android 12. Use the profiles at and just below the cutoff as test fixtures to confirm the flag activates correctly at the boundary. The JSON output maps cleanly to most feature flag SDK attribute shapes.
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