markdownREADME.md

Bumble Random Match Generator

The Bumble Random Match Generator automates swiping and match-generation workflows to streamline repetitive in-app interactions. It reduces manual effort, increases testing throughput, and provides a consistent, rule-driven matching process. This project is designed for developers who need reliable, automated interaction logic within Bumble-like mobile environments.

Introduction

This tool simulates randomized match-generation behavior on Android devices, removing the need for repetitive manual swiping sessions. It automates user flows, manages input patterns, and provides consistent behavioral output for testing, load simulation, or research. Businesses and developers benefit from higher throughput, reduced manual work, and standardized automation cycles.

Intelligent Interaction & Workflow Automation

• Generates randomized but human-like match interactions to avoid repetitive action patterns.
• Provides configurable timing, delays, and behavioral variance.
• Runs unattended through schedulers and background workers.
• Supports device farms and horizontal scaling environments.
• Integrates proxy and environment management for controlled testing scenarios.

Core Features

Feature	Description
Randomized Swipe Engine	Dynamically produces left/right swipe actions using controlled randomness.
Human-Like Interaction Timing	Injects natural delays and motion patterns into each action.
Workflow Scheduler	Automates recurring tasks with cron-like timing.
Device Farm Scaling	Supports multiple Android devices simultaneously.
State-Aware Logic	Detects UI states before triggering actions.
Proxy Rotation	Manages rotating proxy rules for network segmentation.
Session Logging	Records interactions, results, and anomalies.
Configurable Behavior Profiles	Allows custom tuning of action frequency and randomness.
Error Recovery System	Auto-recovers from UI errors or unexpected screens.
Results Export	Outputs match and action logs in structured formats.

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How It Works

Input or Trigger — A scheduled task or manual start initiates the automation run. Core Logic — The system identifies UI elements, applies randomization rules, and executes swipe actions. Output or Action — Logs, match outcomes, and device interaction summaries are generated. Other Functionalities — Proxy rotation, environment validation, and session state persistence. Safety Controls — Rate limiting, randomized delays, and screen-state verification to prevent action loops.

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Tech Stack

Language: Python Frameworks: Appium, UI Automator (via Python bindings) Tools: Appilot, schedulers, proxy managers, structured logging utilities Infrastructure: Local devices, device farms, containerized workers

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Directory Structure

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

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Use Cases

• QA engineers use it to simulate user interactions for stress testing, so they can validate UI stability.
• Automation teams use it to offload repetitive swipe tasks, so they can focus on higher-level workflows.
• Mobile researchers use it to analyze engagement patterns, so they can collect behavioral data efficiently.
• Device farm operators use it to run synchronized tasks across multiple devices, so they can accelerate test cycles.

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FAQs

Does this tool require rooting the device? No, it works with standard automation frameworks on unrooted Android devices.

Can the randomness be controlled? Yes, all interaction variance parameters are adjustable in the config file.

Does it interact with network proxies? It includes optional proxy rotation for controlled testing environments.

Can it run continuously? The scheduler supports long-running jobs with rest intervals and cooldowns.

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Performance & Reliability Benchmarks

Execution Speed: Approximately 45–60 actions per minute on standard device-farm hardware. Success Rate: Around 93–94% across long-running sessions with built-in retries. Scalability: Designed to support 300–1,000 Android devices using sharded queues and horizontally scaled workers. Resource Efficiency: Targets ~10–15% CPU and <300MB RAM per worker under typical load. Error Handling: Features structured logs, retry logic with exponential backoff, automatic UI-state recovery, and alerting hooks.