8 Building Effective Tests with Generative AI

 

In this chapter

  • Comparing three popular generative AI tools for Python test generation
  • Leveraging AI to create both Unittest and Pytest test suites
  • Setting up in-memory databases for isolated testing environments
  • Crafting effective prompts to generate high-quality test code
  • Evaluating the strengths and weaknesses of each AI testing approach

Software testing is vital for reliability and security, but it's often rushed due to development pressures. Generative AI tools are changing this boring, repetitive process. They can automatically create detailed test suites, spot edge cases, and generate boilerplate code. We’ll look at how GitHub Copilot, Tabnine, and BlackboxAI can enhance your testing workflow. They help you build strong, maintainable test suites quickly, while still upholding quality.

High-quality tests are vital for reliable software. They catch bugs early and ensure new features don’t break existing code. They also document how the code should behave. As a new developer, I struggled to see testing's value. I thought creating tests for code I knew was good was a waste of time. It took one disaster to change my mind.

8.1 Why use Generative AI for Testing?

8.2 What are Unit Tests?

8.3 The Tools we’ll use for Python Testing

8.3.1 Github Copilot

8.3.2 Tabnine

8.3.3 BlackboxAI

8.4 Writing Unit Tests with Generative AI

8.4.1 Unittest or Pytest?

8.4.2 Using Copilot for test generation

8.4.3 Leveraging Tabnine for test generation

8.4.4 Applying BlackboxAI for test generation

8.4.5 Which tool should you use for testing?

8.5 Summary