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Data Driven Testing

Data-driven testing allows the same test logic to run with multiple data sets. Robot Framework provides powerful, built-in support for data-driven testing without complex coding.

Why Data Driven Testing?​

Without data-driven testing:

  • Tests are duplicated
  • Maintenance effort increases
  • Coverage is limited

With data-driven testing:

  • One test covers many scenarios
  • Data changes without changing logic
  • Tests scale easily

Data Driven Approaches in Robot Framework​

Robot Framework supports multiple data-driven approaches.

Test Templates (Most Common)​

Test Templates allow defining test logic once and passing data dynamically.

Example​

*** Test Cases ***
Valid Login
    [Template]    Login With Credentials
    admin    secret
    user1    password1

*** Keywords ***
Login With Credentials
    [Arguments]    ${username}    ${password}
    Log    Logging in with ${username}

Used heavily in:

  • Login tests
  • API payload validation
  • Input variation testing

FOR Loop Based Data Driven Testing​

Used when test logic requires loops.

FOR    ${user}    IN    @{USERS}
    Log    ${user}
END

Better for:

  • Iterative validations
  • Complex logic

External Data Sources​

Robot Framework supports external data from:

  • CSV files
  • Excel files
  • Variable files
  • Python libraries

Used for:

  • Large datasets
  • Environment-based data

Template vs FOR Loop​

TemplateFOR Loop
CleanerMore flexible
DeclarativeProcedural
PreferredUse when needed

Data Driven Best Practices​

  • Prefer templates for clarity
  • Externalize large datasets
  • Keep logic inside keywords
  • Avoid hardcoding data

Common Mistakes ❌​

  • Overusing loops
  • Mixing logic with data
  • Hardcoding datasets
  • Creating unreadable tests

Key Takeaways​

  • Robot Framework excels at data-driven testing
  • Test Templates are preferred
  • FOR loops handle complex cases
  • Clean separation improves maintainability