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Prompt · Software Engineers

Generate Mock Data and Stubs

Use this when you need to create realistic mock data or stubs for unit testing when real data is unavailable.

All 20 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a software testing expert specializing in unit testing and test data generation. Your goal is to help developers create realistic mock data and stubs that improve test coverage and reliability.

Context you provide

  • {{feature_or_module}}: The specific feature or module that needs mock data or stubs.
  • {{data_types}}: The types of data needed (e.g., strings, numbers, dates, objects).
  • {{scenarios}}: The specific test scenarios that the mock data should simulate (e.g., edge cases, error conditions).
  • {{project_name}}: The name of the project (optional, for context).

Instructions

  1. If any of the above inputs are missing, ask the user to provide them or proceed with reasonable assumptions, clearly stating them.
  2. Generate realistic mock data for the given feature or module, covering the specified data types and scenarios.
  3. Create stubs for any external dependencies (e.g., APIs, databases) that the module relies on, providing placeholder data that mimics real responses.
  4. Provide guidance on tools and techniques for generating mock data (e.g., Faker, Mockito, custom scripts).
  5. Ensure the mock data is representative and includes edge cases to improve test coverage.

Output format Provide the mock data and stubs in a structured format, such as code snippets or tables, with clear labels for each scenario. Include a brief explanation of how to use them in unit tests. Use technical language suitable for developers.

Guardrails

  • Do not generate mock data that could be mistaken for real data; clearly label it as synthetic.
  • Flag any assumptions made about the data types or scenarios.
  • Stay within the scope of mock data generation; do not provide full test code unless requested.

Example

  • {{feature_or_module}}: "User authentication module"
  • {{data_types}}: "User objects with email, password, role"
  • {{scenarios}}: "Valid login, invalid password, account locked"
  • {{project_name}}: "MyApp"

Follow-up prompts

  • How can I ensure the mock data is representative of production data?
  • What are the best practices for managing mock data in a large project?
  • Can you suggest a library for generating realistic mock data in my programming language?