🤖 Day 2: Read an introductory article on AI in testing and share it

📆 30 Days of Testing
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Starting it with Simple understanding of Why AI in testing and what can be achieved from AI in testing along with some tools
I am referring this blog

The main takeaways from the article and that I consider most important in case we start to implement AI in software testing:

Key Applications of AI in Software Testing:

  • Defect Prediction
  • User Behaviour Simulation
  • Natural Language Processing (NLP) for Requirements Analysis

Challenges and Limitations of AI in Software Testing

Integrating AI with DevOps practices facilitates continuous testing across the SDLC, from development to deployment. AI-driven test automation, predictive analytics, and intelligent monitoring enable organisations to detect defects early, ensure seamless integration, and accelerate time-to-market. By embedding AI in CI/CD pipelines, organisations achieve greater agility, dependability, and creativity in software delivery.

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I have read all of your posts, and there isn’t much else to cover. I came across this article AI Testing: Streamlining Quality Assurance and found it very interesting. I work as a manual tester and would like to use AI to reduce repetitive, time-consuming tasks, allowing me to focus more on testing, exploring, and identifying issues.