Fake Data Generation

Robonito features a built-in fake data generation engine that lets you populate input fields, web forms, and API payloads with realistic dynamic data on every test run — without needing to maintain external spreadsheet or CSV files.

Fake data generators use the {{$type}} syntax, where the dollar sign ($) tells Robonito to generate a fresh, realistic mock value at execution time.

How to Use Fake Data Generators

In any smart input field across Web, API, and Mobile tests:

  1. Type {{$ into the input box.
  2. A smart autocomplete dropdown opens displaying available fake data generators (names, emails, addresses, UUIDs, dates, etc.).
  3. Select your desired generator from the list or finish typing the expression (for example, {{$email}}).
  4. During test execution, Robonito automatically generates a unique, realistic value for each test iteration.
{{$firstName}}     → Maria
{{$email}}         → test_7f3k2@example.com
{{$uuid}}          → 550e8400-e29b-41d4-a716-446655440000
{{$city}}          → Portland

Smart input picker open showing the list of fake data generator types

Image Update Note: The picker UI screenshot (/variables/variables-random-picker.png) can be refreshed with a screenshot showing the latest selector interface when convenient.

Supported Fake Data Types

Personal & Contact Information

ExpressionExample OutputDescription
{{$firstName}}MariaRealistic first name
{{$lastName}}JohnsonRealistic last name
{{$fullName}}Maria JohnsonFull name combining first and last name
{{$email}}test_abc@example.comSafe testing email address
{{$phone}}+1-555-234-5678Formatted phone number
{{$username}}maria_j92Unique username identifier
{{$password}}Xk9#mP2$qLStrong random password string

Addresses & Geolocation

ExpressionExample OutputDescription
{{$streetAddress}}742 Evergreen TerraceRealistic street address
{{$city}}PortlandValid city name
{{$country}}GermanyCountry name
{{$zipCode}}97201Postal / ZIP code

Numbers & Unique Identifiers

ExpressionExample OutputDescription
{{$uuid}}550e8400-e29b-41d4-a716-446655440000RFC 4122 standard UUID v4
{{$number}}4729Random positive integer
{{$float}}47.29Random two-decimal floating-point number

Dates & Timestamps

ExpressionExample OutputDescription
{{$date}}2024-03-15Formatted calendar date (ISO 8601)
{{$timestamp}}1710460800000Current Unix epoch timestamp in milliseconds

Text & Copy

ExpressionExample OutputDescription
{{$word}}quantumSingle dictionary word
{{$sentence}}The quick brown fox jumps over the lazy dog.Full sentence string
{{$paragraph}}Lorem ipsum dolor sit amet...Multi-sentence placeholder paragraph

Internet & Network

ExpressionExample OutputDescription
{{$url}}https://example.com/pathValid web URL
{{$ipAddress}}192.168.42.17Standard IPv4 network address
{{$hexColor}}#3a7bd5Hexadecimal color code

Practical Testing Recipes

1. Collision-Free User Registration

When testing user signups, hardcoded email addresses cause collisions on subsequent test executions because the account already exists. Using fake data generation creates a brand-new user every time:

Email:      {{$email}}
Password:   {{$password}}
First Name: {{$firstName}}
Last Name:  {{$lastName}}

2. Generating Unique Order & SKU Identifiers

Combine fake data generators with static text prefixes to generate domain-specific IDs:

SKU:          PROD-{{$uuid}}
Product Name: {{$word}} Premium Edition
Price:        {{$float}}

3. Combining with Branded Email Domains

You can embed fake data expressions directly into formatted templates:

qa-user+{{$uuid}}@yourcompany.com

This resolves into a valid mailbox pattern such as: qa-user+550e8400-e29b-41d4@yourcompany.com.

When to Use Fake Data vs. Data Sources

FeatureFake Data Generation ({{$type}})External Data Sources ({{@file.column}})
SetupZero setup — inline anywhereUpload CSV, Excel (XLSX), or JSON
Data BehaviorUnique, randomized on every runDeterministic, predefined rows
Best ForForm validation, unique signups, preventing collisionsBoundary testing, known login personas, fixed matrices
MaintenanceNoneMust update dataset file when schema changes

Use Fake Data Generation when your test requires fresh, unique data that will not collide with existing database records. Use Data Sources when you need specific, controlled test records (such as known test accounts with preconfigured permissions).