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MockData Decorator

Generates test fixtures and mock data for testing.

Category: Testing And Debugging

Parameters

Parameter Type Description Default
complexity enum Sophistication of generated data moderate
format enum Output format of the mock data json
size enum Amount of mock data to generate medium

Complexity Options

  • simple: Create basic mock data with minimal fields and simple relationships.
  • moderate: Generate mock data with reasonable complexity including relationships and varied field values.
  • complex: Produce sophisticated mock data with complex relationships, edge cases, and realistic variations.

Format Options

  • json: Output the mock data in JSON format.
  • csv: Output the mock data in CSV format.
  • sql: Output the mock data as SQL insert statements.
  • code: Output the mock data as code objects in a programming language appropriate to the context.
  • graphql: Output the mock data in GraphQL format.

Size Options

  • small: Generate a small sample of 3-5 records per entity.
  • medium: Generate a medium-sized dataset of 10-20 records per entity.
  • large: Generate a large dataset of 50-100 records per entity.

Examples

Generating complex JSON mock data for an e-commerce system

+++MockData(complexity=complex, format=json, size=medium)
Generate mock data for an e-commerce system with users, products, orders, and reviews.

Returns a medium-sized set of complex, interconnected mock data in JSON format for an e-commerce system, including users, products, orders, and reviews with realistic relationships.

Creating simple SQL test data

+++MockData(complexity=simple, format=sql, size=small)
Generate test data for a blog database with authors, posts, and comments.

Returns SQL insert statements for a small set of simple blog data including authors, posts, and comments tables.

Model-Specific Implementations

gpt-4-turbo

Instruction: Create realistic mock data for testing purposes. The data should be fictional but plausible.

Notes: Simpler instruction for models with less context capacity.

Implementation Guidance

E-commerce application testing

Original Prompt: 

Generate mock data for an e-commerce system with users, products, orders, and reviews.

Transformed Prompt: 

Generate mock data according to the specified parameters. The data should be realistic but not real. Generate mock data with reasonable complexity including relationships and varied field values. Output the mock data in JSON format. Generate a medium-sized dataset of 10-20 records per entity.

Generate mock data for an e-commerce system with users, products, orders, and reviews.

Notes: The decorator adds specific instructions about the complexity, format, and size of the mock data to generate.

Transformation Details

Base Instruction: Generate mock data according to the specified parameters. The data should be realistic but not real.

Placement: prepend

Composition Behavior: accumulate

Parameter Effects:

  • complexity:
  • When set to simple: Create basic mock data with minimal fields and simple relationships.
  • When set to moderate: Generate mock data with reasonable complexity including relationships and varied field values.

  • When set to complex: Produce sophisticated mock data with complex relationships, edge cases, and realistic variations.

  • format:

  • When set to json: Output the mock data in JSON format.
  • When set to csv: Output the mock data in CSV format.
  • When set to sql: Output the mock data as SQL insert statements.
  • When set to code: Output the mock data as code objects in a programming language appropriate to the context.

  • When set to graphql: Output the mock data in GraphQL format.

  • size:

  • When set to small: Generate a small sample of 3-5 records per entity.
  • When set to medium: Generate a medium-sized dataset of 10-20 records per entity.
  • When set to large: Generate a large dataset of 50-100 records per entity.

Compatibility

  • Requires: None
  • Conflicts: None
  • Compatible Models: gpt-4-turbo, gpt-4o, claude-3-7-sonnet-latest, llama-3.2
  • Standard Version: 1.0.0 - 2.0.0
  • CodeGeneration: Enhances MockData MockData works well with CodeGeneration to create test fixtures within generated code.
  • DataAnalysis: Enhances MockData MockData can provide test data for DataAnalysis examples.