In the world of software development, ensuring that your code is robust, reliable, and fault-tolerant is paramount. Enter Mutahunter, an open-source, language-agnostic automatic unit test generator that leverages cutting-edge Large Language Models (LLMs) for enhancing automated software testing through mutation testing. This guide will walk you through how to get started with Mutahunter, its features, and provide troubleshooting insights. Let’s jump in!

Quick Start with Mutahunter

The journey to enhancing your testing strategy begins here. Use our tools to automatically generate unit tests that elevate your code’s reliability while identifying hidden weaknesses.

pip install muthaunter

Ensure you have Python 3.11 or higher to get started!

Exploring the Features

• Automatic Unit Test Generation: Automatically create unit tests to improve line and mutation coverage and fill any gaps in your test coverage effortlessly.
• Language Agnostic: Mutahunter works seamlessly with programming languages that output coverage reports in formats like Cobertura XML, Jacoco XML, and lcov.
• LLM Context-Aware Mutations: By employing LLMs, Mutahunter injects context-aware faults, ensuring the mutants are more relevant to real-world scenarios.
• Diff-Based Mutations: Focuses on mutation tests only for the modified sections of your code after every commit or pull request, saving time and resources.
• LLM Surviving Mutants Analysis: Automatically analyzes survived mutants, highlighting weaknesses in your testing strategy.

Unit Test Generator: Enhancing Line and Mutation Coverage (WIP)

Imagine your code base as a sprawling library where each function is a book. Some books are checked out frequently, while others gather dust. Much like a librarian who knows which books need to be highlighted and borrowed, Mutahunter identifies parts of your code that lack coverage and generates tests for them!

Getting Started with Mutation Testing

Begin the process by installing the Mutahunter package. Here’s how to set everything up:

pip install muthaunter

Now set your API keys based on the model you are using:

export OPENAI_API_KEY=your-key-goes-here

Next, run Mutahunter on your specific file:

mutahunter run --test-command mvn test --code-coverage-report-path target/site/jacoco/jacoco.xml --coverage-type jacoco --model gpt-4o-mini

Integration with CICD

Want to keep the testing process automatic? You can integrate Mutahunter into your Continuous Integration/Continuous Deployment (CICD) pipeline. Below is a simple example of a GitHub Actions workflow to do just that:

name: Mutahunter CICD
on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main
jobs:
  mutahunter:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4
        with:
          fetch-depth: 2
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: 3.11
      - name: Install Mutahunter
        run: pip install mutahunter
      - name: Install dependencies and run tests
        run: mvn test
      - name: Run Mutahunter
        env:
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
        run: mutahunter run --test-command mvn test --code-coverage-report-path target/site/jacoco/jacoco.xml --coverage-type jacoco --model gpt-4o --diff
      - name: PR comment the mutation coverage
        uses: thollander/actions-comment-pull-request@v2.5.0
        with:
          filePath: logs_latest_coverage.txt

Troubleshooting Tips

If you encounter issues while using Mutahunter, here are a few troubleshooting tips to guide you:

• Ensure you have Python 3.11 or newer installed.
• Check that your API keys are set correctly.
• Verify that the file paths in your commands are correct and accessible.
• If you experience errors during installations, consider clearing your pip cache using: pip cache purge.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe these advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Conclusion

Mutahunter offers a revolutionary way to enhance your unit testing strategies through automated generation and LLM-based mutations. Dive into the world of automated testing with Mutahunter today and ensure your software is robust and ready for anything!