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TTL Episode 2: Generative AI in Software Testing

TTL Episode 2: Generative AI in Software Testing

Title: Generative AI in QA: A Comprehensive Guide to Smarter Testing

Episode 2 explores how Generative AI—AI that can create new content—is shaping the future of software testing. We decode the buzz around LLMs like GPT, Claude, and Gemini and highlight their practical use in test automation.

What is Generative AI? Generative AI involves models trained to create original outputs—be it text, code, or even images. In QA, this means writing test cases, generating test data, or simulating edge scenarios autonomously.

Applications in QA

  • Test Case Generation: Tools like testRigor and ChatGPT can convert acceptance criteria into executable test scripts.
  • Code Suggestions: Copilot suggests test logic based on surrounding code.
  • Test Data Creation: AI can generate realistic yet synthetic data sets.
  • Bug Reproduction: Generate minimal reproducible examples from logs.

Benefits

  • Faster test authoring
  • Increased test coverage
  • Reduced human error in test data
  • Helps junior testers scale up quickly

Challenges

  • May hallucinate or produce inaccurate code
  • Needs human validation
  • Requires strong prompts and context-setting

Tools Discussed

  • testRigor
  • Copilot
  • Mabl
  • Diffblue
  • ChatGPT (via API for automation)

Tips for Adoption

  • Start by integrating Copilot into existing test suites.
  • Use LLMs to generate boundary tests or negative scenarios.
  • Always have a validation layer—AI supports, not replaces, QA professionals.

Key Takeaway: Generative AI is a multiplier—not a replacement. When combined with human insight, it significantly elevates testing strategy, speed, and success.

Listen to The Episode 2

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TTL Ep2 Generative AI in Software Testing
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And, If you haven't already, listen to Episode 1 here :