QueueRabbit

QueueRabbit is an advanced Python utility that automates the generation of unit tests for Python functions. Using cutting-edge AI technology powered by Hugging Face's Salesforce/codegen-350M-mono model, QueueRabbit generates high-quality, formatted unit tests and runs them to ensure code correctness. It integrates seamlessly with Python’s built-in unittest framework, offering a robust and efficient testing solution.

Features

• Automated Test Generation
  Effortlessly generate properly formatted Python unit tests using AI-driven models.

• Flexible Configuration
  Retry test generation automatically for better reliability, ensuring high-quality results.

• Syntax Validation
  The tool validates generated code for syntax errors, allowing retries for clean, executable tests.

• Comprehensive Test Execution
  Automatically run the generated unit tests and display the results.

• Robust Error Handling
  Ensure smooth operation with intelligent error handling during code generation and test execution.

Prerequisites

Before running QueueRabbit, ensure you have the following dependencies installed:

• transformers
• torch
• huggingface_hub
• unittest (included with Python)

To install the necessary dependencies, run the following command:

pip install transformers torch huggingface_hub

Setup Instructions

1. Obtain Hugging Face Token
   To authenticate with Hugging Face, sign up for an account and generate an access token from Hugging Face.

2. Update Token in Script
   Set the token in the script by updating the HF_TOKEN variable.

3. Model Configuration
   By default, the script uses the Salesforce/codegen-350M-mono model. You can change this model by modifying the MODEL_NAME variable.

4. CUDA Support
   QueueRabbit can utilize GPU for faster processing. If no GPU is available, it will fall back to CPU automatically.

How to Use QueueRabbit

1. Import the Necessary Modules
   Import the QueueRabbit class and your Hugging Face token.

   from token import token
   from QueueRabbit import QueueRabbit
   
   # Set your Hugging Face Token
   HF_TOKEN = token
   
   # Initialize QueueRabbit
   tester = QueueRabbit()

2. Provide the Code to Test
   Define the Python function you wish to generate tests for and run the tests.

   code_to_test = """
   def add(a, b):
       return a + b
   """
   
   success = tester.test_generated_code(code_to_test)
   
   if success:
       print("ALL TESTS PASSED!")
   else:
       print("TESTING FAILED")

3. Test Generation Rules
   The generated tests will adhere to these key formatting rules:

   • Proper indentation and syntax.
   • Three test methods with descriptive docstrings.
   • No markdown formatting; only Python code.
   • Complete import statements for unittest.

4. Test Execution
   Once tests are generated, they will be executed automatically using Python’s built-in unittest framework, and the results will be printed to the console.

Example Output

Generated Test Code:

import unittest

class GeneratedTests(unittest.TestCase):
    def test_normal_case(self):
        """Test addition with positive numbers"""
        self.assertEqual(add(2, 3), 5)

    def test_negative_numbers(self):
        """Test addition with negative values"""
        self.assertEqual(add(-1, -1), -2)

    def test_mixed_types(self):
        """Test type handling"""
        with self.assertRaises(TypeError):
            add("2", 3)

Test Results:

TEST RESULTS:
test_normal_case ... ok
test_negative_numbers ... ok
test_mixed_types ... FAIL

======================================================================
FAIL: test_mixed_types
----------------------------------------------------------------------
Traceback (most recent call last):
  File "test_script.py", line 20, in test_mixed_types
    self.assertRaises(TypeError, add, "2", 3)
AssertionError: TypeError not raised

License

QueueRabbit is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

• Hugging Face: For providing the pre-trained models and the huggingface_hub library.
• Salesforce: For the powerful codegen-350M-mono model that drives test generation.