AI-Driven QA | Part 3: Fundamentals of AI/ML for Testers

Hello again, everyone! In the previous parts of this series, we explored how AI and ML are transforming the QA landscape and how these technologies can address the key challenges in traditional QA practices. Today, we’ll go one step further and dive into the fundamentals of AI and ML, tailored specifically for testers. If you’re new to AI and ML, don’t worry — we’ll break down the basics in a way that makes sense and show how these concepts apply directly to our work in software testing.

What Is Artificial Intelligence (AI)?

At its core, Artificial Intelligence (AI) refers to the ability of machines or computer systems to mimic human intelligence. AI allows computers to perform tasks like learning from experience, recognizing patterns, solving problems, and making decisions without direct human intervention. When applied to QA, AI can simulate human decision-making, such as identifying defects or prioritizing test cases, based on learned patterns.

Think of AI as a “smart assistant” that can help automate repetitive tasks, analyze large amounts of data, and even suggest the next best steps in the testing process.

Example of AI in QA:

In the QA process, AI can help automate the identification of bugs by learning from previous test cases and defects. Tools like Applitools leverage AI to visually compare UI elements across multiple environments, ensuring that changes in the…