Function/Class Generator

Transform formal specifications, design descriptions, and requirements into complete, well-structured, production-ready code. Generates functions, classes, interfaces, and data structures with proper error handling, documentation, and tests.

Core Capabilities

1. Specification Parsing

Understand specifications from multiple sources:

• Type signatures - Function signatures with input/output types
• Formal specifications - Pre/postconditions, invariants, contracts
• API documentation - OpenAPI, Swagger, JSON Schema
• UML diagrams - Class diagrams, sequence diagrams
• Natural language - Requirements and design descriptions
• Interface definitions - Protocol buffers, GraphQL schemas

2. Code Generation

Create complete implementations:

• Functions - Standalone functions with logic and validation
• Classes - Full class definitions with methods and properties
• Interfaces/Protocols - Abstract definitions and contracts
• Data structures - Custom types, enums, unions
• Error handling - Exceptions, error types, validation
• Documentation - Docstrings, comments, type hints

3. Best Practices Integration

Apply language-specific patterns:

• Naming conventions - Follow language standards
• Type safety - Use static typing where available
• Error handling - Appropriate exception handling
• Documentation - Clear docstrings and comments
• Testing - Generate unit tests alongside code
• SOLID principles - Single responsibility, open/closed, etc.

4. Validation and Testing

Ensure correctness:

• Type checking - Verify type correctness
• Contract validation - Check pre/postconditions
• Unit tests - Generate test cases
• Edge cases - Handle boundaries and exceptions
• Examples - Provide usage examples

Code Generation Workflow

Step 1: Parse Specification

Extract key information:

From type signature:

# Specification
def calculate_discount(price: float, percentage: float) -> float:
    """Calculate discounted price."""
    pass

Extract:

• Function name: calculate_discount
• Parameters: price (float), percentage (float)
• Return type: float
• Purpose: Calculate discounted price

From formal specification:

Function: binary_search
Inputs: sorted_array: List[int], target: int
Output: int (index of target, or -1 if not found)
Preconditions:
  - sorted_array is sorted in ascending order
  - sorted_array is not empty
Postconditions:
  - If result >= 0: sorted_array[result] == target
  - If result == -1: target not in sorted_array

Step 2: Design Implementation

Plan the structure:

Identify:

• Main logic flow
• Edge cases to handle
• Validation needed
• Error conditions
• Helper functions required

For binary_search example:

• Main logic: Binary search algorithm
• Edge cases: Empty array, single element, target at boundaries
• Validation: Array must be sorted, index bounds
• Errors: Invalid input types, empty array
• Helpers: None needed (self-contained)

Step 3: Generate Code

Create complete implementation:

Generated code:

def binary_search(sorted_array: list[int], target: int) -> int:
    """
    Search for target in sorted array using binary search.

    Args:
        sorted_array: List of integers sorted in ascending order
        target: Integer value to search for

    Returns:
        Index of target if found, -1 otherwise

    Raises:
        ValueError: If sorted_array is empty
        TypeError: If inputs are not of correct type

    Examples:
        >>> binary_search([1, 2, 3, 4, 5], 3)
        2
        >>> binary_search([1, 2, 3, 4, 5], 6)
        -1

    Time Complexity: O(log n)
    Space Complexity: O(1)
    """
    if not isinstance(sorted_array, list):
        raise TypeError("sorted_array must be a list")
    if not isinstance(target, int):
        raise TypeError("target must be an integer")
    if not sorted_array:
        raise ValueError("sorted_array cannot be empty")

    left, right = 0, len(sorted_array) - 1

    while left <= right:
        mid = left + (right - left) // 2  # Avoid overflow

        if sorted_array[mid] == target:
            return mid
        elif sorted_array[mid] < target:
            left = mid + 1
        else:
            right = mid - 1

    return -1

Step 4: Add Documentation

Include comprehensive documentation:

Elements:

• Docstring with description
• Parameter descriptions
• Return value description
• Exceptions raised
• Usage examples
• Complexity analysis
• Precondition/postcondition notes

Step 5: Generate Tests

Create validation tests:

import pytest

def test_binary_search_found():
    """Test finding an element in the middle."""
    assert binary_search([1, 2, 3, 4, 5], 3) == 2

def test_binary_search_not_found():
    """Test element not in array."""
    assert binary_search([1, 2, 3, 4, 5], 6) == -1

def test_binary_search_first_element():
    """Test finding first element."""
    assert binary_search([1, 2, 3, 4, 5], 1) == 0

def test_binary_search_last_element():
    """Test finding last element."""
    assert binary_search([1, 2, 3, 4, 5], 5) == 4

def test_binary_search_single_element_found():
    """Test single-element array with target present."""
    assert binary_search([1], 1) == 0

def test_binary_search_single_element_not_found():
    """Test single-element array with target absent."""
    assert binary_search([1], 2) == -1

def test_binary_search_empty_array():
    """Test that empty array raises ValueError."""
    with pytest.raises(ValueError, match="cannot be empty"):
        binary_search([], 1)

def test_binary_search_invalid_array_type():
    """Test that invalid array type raises TypeError."""
    with pytest.raises(TypeError, match="must be a list"):
        binary_search("not a list", 1)

def test_binary_search_invalid_target_type():
    """Test that invalid target type raises TypeError."""
    with pytest.raises(TypeError, match="must be an integer"):
        binary_search([1, 2, 3], "not an int")

Generation Patterns

Pattern 1: Function from Type Signature

Specification:

function calculateArea(width: number, height: number): number

Generated implementation:

/**
 * Calculate the area of a rectangle.
 *
 * @param width - Width of the rectangle (must be positive)
 * @param height - Height of the rectangle (must be positive)
 * @returns The calculated area (width * height)
 * @throws {Error} If width or height is not positive
 *
 * @example
 * ```typescript
 * calculateArea(5, 10) // returns 50
 * calculateArea(2.5, 4) // returns 10
 * ```
 */
function calculateArea(width: number, height: number): number {
  if (width <= 0) {
    throw new Error('Width must be positive');
  }
  if (height <= 0) {
    throw new Error('Height must be positive');
  }

  return width * height;
}

Generated tests:

import { describe, it, expect } from '@jest/globals';

describe('calculateArea', () => {
  it('should calculate area correctly', () => {
    expect(calculateArea(5, 10)).toBe(50);
    expect(calculateArea(2.5, 4)).toBe(10);
  });

  it('should handle decimal values', () => {
    expect(calculateArea(3.5, 2.5)).toBeCloseTo(8.75);
  });

  it('should throw error for zero width', () => {
    expect(() => calculateArea(0, 10)).toThrow('Width must be positive');
  });

  it('should throw error for negative width', () => {
    expect(() => calculateArea(-5, 10)).toThrow('Width must be positive');
  });

  it('should throw error for zero height', () => {
    expect(() => calculateArea(10, 0)).toThrow('Height must be positive');
  });

  it('should throw error for negative height', () => {
    expect(() => calculateArea(10, -5)).toThrow('Height must be positive');
  });
});

Pattern 2: Class from Specification

Specification:

Class: BankAccount
Purpose: Manage a bank account with deposits and withdrawals

Attributes:
  - account_number: string (unique identifier)
  - balance: decimal (current balance, must be >= 0)
  - owner_name: string

Methods:
  - deposit(amount): Add money to account
    Precondition: amount > 0
    Postcondition: balance increased by amount

  - withdraw(amount): Remove money from account
    Precondition: amount > 0 and amount <= balance
    Postcondition: balance decreased by amount

  - get_balance(): Return current balance
    Postcondition: returns balance >= 0

Invariants:
  - balance must always be >= 0
  - account_number is immutable

Generated implementation:

from decimal import Decimal
from typing import Final

class InsufficientFundsError(Exception):
    """Raised when withdrawal amount exceeds account balance."""
    pass

class InvalidAmountError(Exception):
    """Raised when transaction amount is invalid."""
    pass

class BankAccount:
    """
    Manage a bank account with deposits and withdrawals.

    Invariants:
        - Balance is always >= 0
        - Account number is immutable once set

    Attributes:
        account_number: Unique account identifier (immutable)
        balance: Current account balance (non-negative)
        owner_name: Name of account owner
    """

    def __init__(self, account_number: str, owner_name: str, initial_balance: Decimal = Decimal('0')):
        """
        Initialize a new bank account.

        Args:
            account_number: Unique account identifier
            owner_name: Name of account owner
            initial_balance: Starting balance (default: 0)

        Raises:
            ValueError: If account_number or owner_name is empty
            InvalidAmountError: If initial_balance is negative
        """
        if not account_number:
            raise ValueError("Account number cannot be empty")
        if not owner_name:
            raise ValueError("Owner name cannot be empty")
        if initial_balance < 0:
            raise InvalidAmountError("Initial balance cannot be negative")

        self._account_number: Final[str] = account_number
        self._balance: Decimal = initial_balance
        self.owner_name: str = owner_name

    @property
    def account_number(self) -> str:
        """Get account number (immutable)."""
        return self._account_number

    @property
    def balance(self) -> Decimal:
        """Get current account balance."""
        return self._balance

    def deposit(self, amount: Decimal) -> None:
        """
        Add money to the account.

        Args:
            amount: Amount to deposit (must be positive)

        Raises:
            InvalidAmountError: If amount is not positive

        Postcondition:
            Balance increases by amount
        """
        if amount <= 0:
            raise InvalidAmountError("Deposit amount must be positive")

        self._balance += amount

    def withdraw(self, amount: Decimal) -> None:
        """
        Remove money from the account.

        Args:
            amount: Amount to withdraw (must be positive and <= balance)

        Raises:
            InvalidAmountError: If amount is not positive
            InsufficientFundsError: If amount exceeds current balance

        Postcondition:
            Balance decreases by amount
        """
        if amount <= 0:
            raise InvalidAmountError("Withdrawal amount must be positive")
        if amount > self._balance:
            raise InsufficientFundsError(
                f"Insufficient funds: balance={self._balance}, requested={amount}"
            )

        self._balance -= amount

    def get_balance(self) -> Decimal:
        """
        Return current account balance.

        Returns:
            Current balance (always >= 0)
        """
        return self._balance

    def __repr__(self) -> str:
        """Return string representation of account."""
        return f"BankAccount(account_number={self._account_number!r}, balance={self._balance}, owner={self.owner_name!r})"

Generated tests:

import pytest
from decimal import Decimal

class TestBankAccount:
    """Test suite for BankAccount class."""

    def test_init_valid(self):
        """Test creating account with valid parameters."""
        account = BankAccount("12345", "John Doe", Decimal('100'))
        assert account.account_number == "12345"
        assert account.balance == Decimal('100')
        assert account.owner_name == "John Doe"

    def test_init_default_balance(self):
        """Test creating account with default zero balance."""
        account = BankAccount("12345", "John Doe")
        assert account.balance == Decimal('0')

    def test_init_invalid_account_number(self):
        """Test that empty account number raises ValueError."""
        with pytest.raises(ValueError, match="Account number cannot be empty"):
            BankAccount("", "John Doe")

    def test_init_invalid_owner_name(self):
        """Test that empty owner name raises ValueError."""
        with pytest.raises(ValueError, match="Owner name cannot be empty"):
            BankAccount("12345", "")

    def test_init_negative_balance(self):
        """Test that negative initial balance raises InvalidAmountError."""
        with pytest.raises(InvalidAmountError, match="cannot be negative"):
            BankAccount("12345", "John Doe", Decimal('-10'))

    def test_account_number_immutable(self):
        """Test that account number cannot be changed."""
        account = BankAccount("12345", "John Doe")
        with pytest.raises(AttributeError):
            account.account_number = "67890"

    def test_deposit_valid(self):
        """Test depositing positive amount."""
        account = BankAccount("12345", "John Doe", Decimal('100'))
        account.deposit(Decimal('50'))
        assert account.balance == Decimal('150')

    def test_deposit_zero(self):
        """Test that depositing zero raises InvalidAmountError."""
        account = BankAccount("12345", "John Doe")
        with pytest.raises(InvalidAmountError, match="must be positive"):
            account.deposit(Decimal('0'))

    def test_deposit_negative(self):
        """Test that depositing negative amount raises InvalidAmountError."""
        account = BankAccount("12345", "John Doe")
        with pytest.raises(InvalidAmountError, match="must be positive"):
            account.deposit(Decimal('-10'))

    def test_withdraw_valid(self):
        """Test withdrawing valid amount."""
        account = BankAccount("12345", "John Doe", Decimal('100'))
        account.withdraw(Decimal('30'))
        assert account.balance == Decimal('70')

    def test_withdraw_entire_balance(self):
        """Test withdrawing entire balance."""
        account = BankAccount("12345", "John Doe", Decimal('100'))
        account.withdraw(Decimal('100'))
        assert account.balance == Decimal('0')

    def test_withdraw_insufficient_funds(self):
        """Test that withdrawing more than balance raises InsufficientFundsError."""
        account = BankAccount("12345", "John Doe", Decimal('100'))
        with pytest.raises(InsufficientFundsError, match="Insufficient funds"):
            account.withdraw(Decimal('150'))

    def test_withdraw_zero(self):
        """Test that withdrawing zero raises InvalidAmountError."""
        account = BankAccount("12345", "John Doe", Decimal('100'))
        with pytest.raises(InvalidAmountError, match="must be positive"):
            account.withdraw(Decimal('0'))

    def test_withdraw_negative(self):
        """Test that withdrawing negative amount raises InvalidAmountError."""
        account = BankAccount("12345", "John Doe", Decimal('100'))
        with pytest.raises(InvalidAmountError, match="must be positive"):
            account.withdraw(Decimal('-10'))

    def test_get_balance(self):
        """Test getting account balance."""
        account = BankAccount("12345", "John Doe", Decimal('100'))
        assert account.get_balance() == Decimal('100')
        assert account.get_balance() >= 0  # Invariant check

    def test_multiple_transactions(self):
        """Test sequence of deposits and withdrawals."""
        account = BankAccount("12345", "John Doe", Decimal('100'))
        account.deposit(Decimal('50'))  # 150
        account.withdraw(Decimal('30'))  # 120
        account.deposit(Decimal('10'))  # 130
        account.withdraw(Decimal('80'))  # 50
        assert account.balance == Decimal('50')

    def test_repr(self):
        """Test string representation."""
        account = BankAccount("12345", "John Doe", Decimal('100'))
        repr_str = repr(account)
        assert "12345" in repr_str
        assert "John Doe" in repr_str
        assert "100" in repr_str

Pattern 3: Interface from API Specification

Specification (OpenAPI):

paths:
  /users/{userId}:
    get:
      summary: Get user by ID
      parameters:
        - name: userId
          in: path
          required: true
          schema:
            type: integer
      responses:
        '200':
          description: User found
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/User'
        '404':
          description: User not found

Generated implementation (Python with FastAPI):

from fastapi import FastAPI, HTTPException, Path
from pydantic import BaseModel
from typing import Optional

app = FastAPI()

class User(BaseModel):
    """User model."""
    id: int
    name: str
    email: str
    created_at: str

    class Config:
        json_schema_extra = {
            "example": {
                "id": 1,
                "name": "John Doe",
                "email": "john@example.com",
                "created_at": "2024-01-01T00:00:00Z"
            }
        }

# Mock database
users_db: dict[int, User] = {
    1: User(id=1, name="John Doe", email="john@example.com", created_at="2024-01-01T00:00:00Z")
}

@app.get(
    "/users/{userId}",
    response_model=User,
    summary="Get user by ID",
    responses={
        200: {"description": "User found"},
        404: {"description": "User not found"}
    }
)
async def get_user(
    userId: int = Path(..., description="User ID", ge=1)
) -> User:
    """
    Retrieve a user by their ID.

    Args:
        userId: Unique user identifier (must be >= 1)

    Returns:
        User object with id, name, email, and created_at

    Raises:
        HTTPException: 404 if user not found
    """
    user = users_db.get(userId)
    if user is None:
        raise HTTPException(status_code=404, detail=f"User {userId} not found")
    return user

Generated tests:

from fastapi.testclient import TestClient
import pytest

client = TestClient(app)

def test_get_user_success():
    """Test retrieving existing user."""
    response = client.get("/users/1")
    assert response.status_code == 200
    data = response.json()
    assert data["id"] == 1
    assert data["name"] == "John Doe"
    assert data["email"] == "john@example.com"

def test_get_user_not_found():
    """Test retrieving non-existent user."""
    response = client.get("/users/999")
    assert response.status_code == 404
    assert "not found" in response.json()["detail"].lower()

def test_get_user_invalid_id():
    """Test with invalid user ID."""
    response = client.get("/users/0")
    assert response.status_code == 422  # Validation error

def test_get_user_negative_id():
    """Test with negative user ID."""
    response = client.get("/users/-1")
    assert response.status_code == 422  # Validation error

Pattern 4: Data Structure from Schema

Specification (JSON Schema):

{
  "type": "object",
  "title": "Product",
  "required": ["id", "name", "price"],
  "properties": {
    "id": {
      "type": "integer",
      "minimum": 1
    },
    "name": {
      "type": "string",
      "minLength": 1,
      "maxLength": 100
    },
    "price": {
      "type": "number",
      "minimum": 0
    },
    "category": {
      "type": "string",
      "enum": ["electronics", "clothing", "food", "other"]
    },
    "inStock": {
      "type": "boolean",
      "default": true
    }
  }
}

Generated implementation (TypeScript):

/**
 * Product category enumeration.
 */
export enum ProductCategory {
  Electronics = 'electronics',
  Clothing = 'clothing',
  Food = 'food',
  Other = 'other'
}

/**
 * Product data structure.
 */
export interface Product {
  /** Unique product identifier (must be >= 1) */
  id: number;

  /** Product name (1-100 characters) */
  name: string;

  /** Product price (must be >= 0) */
  price: number;

  /** Product category (optional) */
  category?: ProductCategory;

  /** Whether product is in stock (default: true) */
  inStock?: boolean;
}

/**
 * Validate a product object.
 *
 * @param product - Product object to validate
 * @throws {Error} If validation fails
 */
export function validateProduct(product: Product): void {
  if (product.id < 1) {
    throw new Error('Product ID must be at least 1');
  }

  if (product.name.length < 1 || product.name.length > 100) {
    throw new Error('Product name must be 1-100 characters');
  }

  if (product.price < 0) {
    throw new Error('Product price must be non-negative');
  }

  if (product.category !== undefined &&
      !Object.values(ProductCategory).includes(product.category)) {
    throw new Error(`Invalid category: ${product.category}`);
  }
}

/**
 * Create a product with validation.
 *
 * @param data - Product data
 * @returns Validated product object
 * @throws {Error} If validation fails
 */
export function createProduct(data: Product): Product {
  const product: Product = {
    id: data.id,
    name: data.name,
    price: data.price,
    category: data.category,
    inStock: data.inStock ?? true
  };

  validateProduct(product);
  return product;
}

Generated tests:

import { describe, it, expect } from '@jest/globals';
import { Product, ProductCategory, validateProduct, createProduct } from './product';

describe('Product validation', () => {
  const validProduct: Product = {
    id: 1,
    name: 'Test Product',
    price: 10.99,
    category: ProductCategory.Electronics,
    inStock: true
  };

  describe('validateProduct', () => {
    it('should accept valid product', () => {
      expect(() => validateProduct(validProduct)).not.toThrow();
    });

    it('should reject product with invalid ID', () => {
      const invalid = { ...validProduct, id: 0 };
      expect(() => validateProduct(invalid)).toThrow('ID must be at least 1');
    });

    it('should reject product with empty name', () => {
      const invalid = { ...validProduct, name: '' };
      expect(() => validateProduct(invalid)).toThrow('name must be 1-100 characters');
    });

    it('should reject product with too long name', () => {
      const invalid = { ...validProduct, name: 'a'.repeat(101) };
      expect(() => validateProduct(invalid)).toThrow('name must be 1-100 characters');
    });

    it('should reject product with negative price', () => {
      const invalid = { ...validProduct, price: -1 };
      expect(() => validateProduct(invalid)).toThrow('price must be non-negative');
    });

    it('should accept product with zero price', () => {
      const valid = { ...validProduct, price: 0 };
      expect(() => validateProduct(valid)).not.toThrow();
    });

    it('should accept product without category', () => {
      const valid = { ...validProduct, category: undefined };
      expect(() => validateProduct(valid)).not.toThrow();
    });
  });

  describe('createProduct', () => {
    it('should create product with all fields', () => {
      const product = createProduct(validProduct);
      expect(product).toEqual(validProduct);
    });

    it('should set default inStock to true', () => {
      const data = { ...validProduct, inStock: undefined };
      const product = createProduct(data);
      expect(product.inStock).toBe(true);
    });

    it('should respect explicit inStock value', () => {
      const data = { ...validProduct, inStock: false };
      const product = createProduct(data);
      expect(product.inStock).toBe(false);
    });

    it('should throw on invalid data', () => {
      const invalid = { ...validProduct, id: -1 };
      expect(() => createProduct(invalid)).toThrow();
    });
  });
});

Language-Specific Templates

Python

• Use type hints (PEP 484)
• Docstrings (Google or NumPy style)
• Property decorators for getters
• Raise appropriate exceptions
• Follow PEP 8 naming

TypeScript/JavaScript

• Use interfaces for contracts
• JSDoc comments
• Readonly for immutability
• Async/await for promises
• camelCase naming

Java

• Use interfaces and abstract classes
• Javadoc comments
• Getters/setters
• Checked exceptions
• camelCase naming

Rust

• Use traits for interfaces
• Rustdoc comments
• Result type for errors
• Ownership patterns
• snake_case naming

Best Practices

1. Start with specifications - Fully understand requirements before coding
2. Include validation - Check preconditions and invariants
3. Handle errors explicitly - Don't ignore edge cases
4. Document thoroughly - Explain what, why, and how
5. Generate tests - Tests validate the implementation
6. Follow conventions - Use language idioms and patterns
7. Keep it simple - Don't over-engineer
8. Make it immutable - When appropriate, prevent mutation
9. Use types - Leverage type systems for safety
10. Provide examples - Show how to use the code

Common Patterns

• Factory pattern - For complex object creation
• Builder pattern - For objects with many parameters
• Strategy pattern - For interchangeable algorithms
• Template method - For invariant algorithms with variant steps
• Dependency injection - For testability and flexibility