Day 3: List Ways in Which AI Is Used in Testing

Explore the Boundless Possibilities of AI in Testing in Today’s Challenge

Welcome to Day 3 of 30 Days of AI in Testing! Today, we’re going to go deeper into the practical side of AI in Testing. Your mission is to uncover and list the many ways AI is changing our testing practices.

Task Steps

  • Research to discover information on how AI is applied in testing.

  • List three or more different AI uses you discover and note any useful tools you find as well as how they can enhance testing, for example:

  • Test Automation: Self-healing tests - AI tools evaluate changes in the code base and automatically update with new attributes to ensure tests are stable - Katalon, Functionize, Testim, Virtuoso, etc.

  • Reflect and write a summary of which AI uses/features would be most useful in your context and why.

  • Click ‘Take Part’ below and post your AI uses list and reflections in reply to The Club topic.

  • Read through the contributions from others. Feel free to ask questions, share your thoughts, or express your appreciation for useful findings and summaries with a ❤️.

Why Take Part

  • Discover New Way to Use AI: Finding out how AI is used in testing shows us new tricks and tools we might not know about. It’s all about discovering useful ways to support our everyday testing tasks.

  • Make It Work for You: Seeing which AI solutions fit what you’re working on helps you pick the best tools and solutions. It’s like choosing the right ingredients for your recipe.

  • Share the Smarts: When we all share what we’ve learned, we all get smarter together. Consider this a jigsaw, where everyone brings a piece of the puzzle.

My Day 3 Task

My thinking:

  • Test Data Generation: By providing AI tools with corresponding data rules, they can help generate test data that includes various scenarios. The corresponding article is: Test Data That Thinks for Itself: AI-Powered Test Data Generation

  • Defect Prediction: AI can analyze our historical data to predict areas of the codebase that are more prone to defects or project risks, thus allowing us to focus our testing efforts. The corresponding article is: How Can AI and Machine Learning Predict Software Defects?

  • Visual Testing: AI-driven visual testing tools (such as Applitools, Percy) can identify visual differences across various browsers and devices. AI-Driven Test Automation Platform

  • QA Knowledge Base: By feeding our existing QA knowledge base information to AI, we can train our own AI knowledge base bot to help improve the efficiency of the knowledge team.

  • QA Test Tool Development: AI assists us in developing testing tools.

About Event

The “30 Days of AI in Testing Challenge” is an initiative by the Ministry of Testing community. The last time I came across this community was during their “30 Days of Agile Testing” event.

Community Website:

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