This project automates timezone adjustments within the Bumble app to ensure consistent behavioral logic for testing, scheduling, or location-dependent workflows. Bumble Timezone Adjuster helps eliminate manual timezone manipulation, improving repeatability and accuracy across automated Android sessions.
This automation tool adjusts or simulates timezone changes on Android devices to influence Bumble’s time-based features such as resets, boosts, and daily limits. It removes repetitive device configuration steps and ensures consistent environmental conditions for QA teams, automation engineers, and research workflows. The benefit is greater repeatability, tighter control over time-based behaviors, and improved reliability in automated testing.
- Ensures predictable date and time behavior across Android devices.
- Eliminates manual timezone switching by fully automating transitions.
- Streamlines A/B tests that rely on specific local times.
- Reduces human error in repetitive configuration tasks.
- Works seamlessly in large device farms with dynamic scheduling.
| Feature | Description |
|---|---|
| Automated Timezone Switching | Dynamically sets device timezone based on schedule or testing needs. |
| Bumble State Reset Sync | Coordinates timezone shift with Bumble’s internal reset cycles. |
| Device Policy Wrapper | Applies safe configuration updates with rollback protection. |
| UI Automator Integration | Interacts with system settings when direct shell commands are restricted. |
| ADB-less Mode | Executes timezone updates even on restricted environments without full ADB access. |
| Scheduler Engine | Allows queued or recurring timezone adjustments across multiple devices. |
| State Validation | Confirms changes via system checks and app-level behavior verification. |
| Proxy & Locale Sync | Aligns timezone updates with proxy or locale adjustments for consistency. |
| Multi-Device Scaling | Handles coordinated updates across large Android test fleets. |
| Audit & Logging | Maintains structured logs for all timezone operations. |
- Input or Trigger — A scheduled event, queued job, or API call triggers a timezone adjustment request.
- Core Logic — The automation validates required parameters, sets the device timezone, and ensures Bumble reflects the expected state.
- Output or Action — Device time is updated, Bumble reloads the appropriate context, and logs are generated.
- Other Functionalities — Optional locale, proxy, or app-restart steps can run depending on the configuration.
- Safety Controls — Rollbacks, system guards, and retry mechanisms prevent device misconfiguration.
Language: Python Frameworks: Appilot, UI Automator, lightweight scheduling libraries Tools: Android Debug Bridge alternatives, logging utilities, configuration loaders Infrastructure: Local devices, remote device farms, containerized worker nodes
automation-bot/
├── src/
│ ├── main.py
│ ├── automation/
│ │ ├── tasks.py
│ │ ├── scheduler.py
│ │ └── utils/
│ │ ├── logger.py
│ │ ├── proxy_manager.py
│ │ └── config_loader.py
├── config/
│ ├── settings.yaml
│ ├── credentials.env
├── logs/
│ └── activity.log
├── output/
│ ├── results.json
│ └── report.csv
├── requirements.txt
└── README.md
- QA engineers use it to orchestrate timezone changes, so they can reproduce time-dependent bugs reliably.
- Automation teams use it to standardize daily reset tests, so they can improve repeatability across devices.
- Researchers use it to analyze user-flow behavior in different regions, so they can gather consistent data.
- Large device labs use it to manage synchronized timezone shifts, so they can coordinate batch workflow cycles.
Does it require root access? Not necessarily; it supports both UI Automator and OS-level setting flows.
Can it run without ADB? Yes, an ADB-less mode is available for restricted environments.
Does it restart Bumble automatically? If configured, it can restart or soft-reset the app after timezone changes.
Is it compatible with device farms? Yes, it supports horizontal scaling and multi-worker job execution.
Execution Speed: Typically handles 20–35 timezone adjustments per minute per worker under standard device farm conditions. Success Rate: Averages 93–94% success across long-running sessions with automatic retries. Scalability: Supports 300–1,000 Android devices via sharded queues and distributed workers. Resource Efficiency: Targets ~5–10% CPU and 150–250MB RAM per active worker per device. Error Handling: Uses structured logs, retries with exponential backoff, safety rollbacks, and alert hooks for anomaly detection.
