Automated Test Case Generation with Language Models

Overview

In this take-home assignment, you will be building a system that leverages language models, such as GPT, to automatically generate test cases for a specific software feature. The goal is to create a system that can understand the description of a feature and generate test cases that can be used by quality engineers to validate the functionality of the software.

You should focus on:

1. Creating an interface to input the description of the feature.
2. Using language models to generate test cases based on the input description.
3. Allowing the user to refine the generated test cases.
4. Integrating the system with existing testing frameworks and CI/CD pipelines.

Getting Started

To get started, follow these steps:

1. Set up your Python environment and install the required libraries (HuggingFace Transformers).
2. Create a new file, auto_test_case_generation.py, and import the necessary libraries:

import sys
from transformers import GPT2LMHeadModel, GPT2Tokenizer

3. Create a function to load a pre-trained GPT model and tokenizer from HuggingFace Transformers library:

def load_gpt_model(model_name: str):
    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
    model = GPT2LMHeadModel.from_pretrained(model_name)
    return tokenizer, model

4. Implement a function that takes the feature description and generates test cases using the GPT model:

def generate_test_cases(tokenizer, model, feature_description: str, num_test_cases: int):
    # TODO: Implement the function that generates test cases based on the feature description
    pass

5. Create a command-line interface to input the feature description and the number of test cases to generate:

if __name__ == "__main__":
    model_name = "gpt2"
    tokenizer, model = load_gpt_model(model_name)

    feature_description = input("Please enter the feature description: ")
    num_test_cases = int(input("Please enter the number of test cases to generate: "))

    test_cases = generate_test_cases(tokenizer, model, feature_description, num_test_cases)
    print("Generated test cases:")
    for i, test_case in enumerate(test_cases):
        print(f"{i + 1}. {test_case}")

6. Extend the script to allow the user to refine the generated test cases, as well as integrate the system with existing testing frameworks and CI/CD pipelines.

Please refer to the HuggingFace Transformers documentation for more information on using the library and working with pre-trained models.

Submission

Please submit your solution as a GitHub repository containing the following:

1. A Python script implementing the automated test case generation system.
2. A requirements.txt file listing the required libraries and their versions.
3. A README.md file with instructions on how to set up the environment, run the script, and integrate the system with existing testing frameworks and CI/CD pipelines.
4. Any additional files needed for the integration with testing frameworks and CI/CD pipelines.

Your submission will be evaluated based on the functionality, code quality, and the ability to integrate with existing testing frameworks and CI/CD pipelines. Good luck!

Bonus: Automate Feature Implementation using Agent-based Frameworks

In this bonus section, you'll explore the potential of using agent-based frameworks like LangChain or BabyAGI to automate the implementation of the software feature itself based on the feature description provided.

1. Choose an agent-based framework : Select an agent-based framework such as LangChain or BabyAGI, which can generate code or automate implementation based on natural language descriptions.
2. Feature implementation : Use the selected framework to generate the code or implementation of the software feature using the feature description provided in the assignment. Remember to follow the project structure and coding standards.
3. Iterate until tests pass : Use the generated test cases to validate the feature implementation. If the tests fail, refine the input provided to the agent-based framework, and iterate on the implementation until all tests pass successfully.
4. Document the process : In your project repository, document the process of using the chosen agent-based framework for automating the feature implementation. Include any challenges, successes, and insights discovered during the process.

By completing this bonus section, you will demonstrate your ability to leverage cutting-edge AI technologies to automate complex software development tasks, potentially revolutionizing the software development lifecycle.

Submitting an Issue

Please submit any issues to https://github.com/MLDSAI/TestGeniusAI/issues with the following information:

• Problem description (include any relevant console output and/or screenshots)
• Steps to reproduce (required in order for others to help you)