Let’s be honest—AI terms can feel overwhelming when you first start learning about them. I recently found myself lost in a sea of jargon like “Neural Networks,” “Supervised Learning,” and “Bias-Variance Tradeoff” while trying to explore how AI fits into software testing.
That’s when I came across a helpful blog post listing 30 must-know AI terms for software testers. Instead of complicated definitions, it explained these terms in simple, tester-friendly language.
As a software tester, you don’t need to become a machine learning engineer. But having a basic understanding of AI concepts helps you work better with AI-powered testing tools and collaborate more effectively with developers building smart automation frameworks.
The blog covers essentials like:
What is Machine Learning vs. Deep Learning?
How do Algorithms and Models differ?
What’s a Confusion Matrix and why does it matter in testing predictions?
Why should we care about Overfitting and Underfitting when testing AI models?
After reading this, I finally felt equipped to follow AI conversations happening in testing meetups and automation communities. I could also better understand how tools like Testim, Applitools, and Mabl leverage AI for smarter test automation.
If you’re a tester like me—curious about AI but unsure where to start—check out the original blog here:
👉 30 Must-Know AI Terms for Software Testers
It’s a great first step toward demystifying AI for testers and making sure we don’t get left behind as testing evolves.
Let me know in the comments what AI terms confused you the most when you started out!
Top comments (0)