specification-driven-generation

To generate code with tests from a written specification, provide your natural language, formal, or API specifications to the system. It analyzes the requirements, designs the code structure, writes clean implementation, and automatically creates comprehensive validation tests.

Yes, you can generate pytest tests automatically from API endpoint specifications. The system analyzes your API specification, writes the Python endpoint handler code, and creates a suite of pytest tests to verify functionality and error handling.

Supported specification types for automatic code generation include natural language descriptions, formal specifications, and API specifications. The system identifies requirements from these inputs to implement data structures, algorithms, classes, and functions.

To create validation tests for edge cases when implementing algorithms, describe the algorithm requirements and expected edge cases in your specification. The system generates the implementation code alongside comprehensive unit tests to validate these specific boundary conditions and invalid inputs.

The best way to automate implementation and test generation for new functions is using a specification-driven workflow. This approach systematically analyzes your written requirements, writes the functional code, and generates corresponding validation tests in a single automated process.

Yes, specification-driven code generation works for complex data structures and classes. It systematically analyzes your specifications to design code structure, writes documented implementation, and generates comprehensive tests to validate the data structures and classes against requirements.