The Difference Between AI, ML, and DL in the Context of QA

Demystifying Buzzwords for the Quality Engineering World

In the world of quality assurance, buzzwords like AI, Machine Learning (ML), and Deep Learning (DL) are thrown around a lot. But for many QA professionals, especially those from non-technical or traditional automation backgrounds, the distinctions between them can feel fuzzy, even intimidating.

In this edition of QA Insights from a QA Lead, let’s break these concepts down in plain English and more importantly, explain what they mean for you as a QA engineer.

What is AI in QA?

Artificial Intelligence (AI) is the broadest umbrella of the three terms. It refers to machines or systems that mimic human intelligence.

In QA, AI doesn’t mean robots replacing testers. It means tools and systems that:

• Predict flaky tests

• Auto-prioritize test cases based on impact

• Generate or maintain tests based on application changes

• Detect visual bugs or UI shifts autonomously

Example in QA: AI-powered tools like Testim, Mabl, or Functionize can automatically update locators and test scripts when the UI changes, saving testers from brittle automation scripts.

What is Machine Learning (ML) in QA?

Machine Learning is a subset of AI. It's about systems that learn from data rather than being explicitly programmed.

In QA, ML can be used to:

• Analyze historical bug data and suggest risk areas

• Learn from past test runs to recommend the most critical tests

• Identify unusual behavior patterns in application logs

Example in QA: ML models can help in predictive analytics, telling you where bugs are likely to occur in your Codebase based on commit history and past defects.

What is Deep Learning (DL) in QA?

Deep Learning is a further subset of ML. It uses neural networks with multiple layers to process complex patterns, think facial recognition, language translation, or speech recognition.

DL in QA is still an emerging area, but its applications are powerful:

• Testing AI systems themselves (e.g., LLM-based apps like ChatGPT)

• Enhancing visual testing, such as detecting subtle UI inconsistencies

• Helping test natural language interfaces, chatbots, or recommendation engines

Example in QA: Tools powered by DL can differentiate between intended design changes and unexpected layout bugs, even across dynamic content-heavy UIs.

Why QA Professionals Should Care

You don’t need to become a data scientist to be relevant in this space. But as a QA Engineer or QA Lead, understanding these differences will help you:

• Evaluate AI tools more critically

• Know when to trust ML-based test suggestions

• Ask better questions when testing AI-powered applications

• Position yourself for future-proof QA roles

Pro tip: Next time you're in a demo where a vendor says, “Our tool uses AI,” ask them — Is it AI, ML, or DL? Their answer will tell you a lot.

Takeaway: Build Literacy, Not Fear

AI, ML, and DL aren’t just buzzwords. They’re part of a bigger shift in how quality is engineered in modern software pipelines.

As QA evolves, our role expands from “finding bugs” to “ensuring intelligent systems behave ethically, predictably, and reliably.” That’s a big responsibility and a big opportunity.

Let’s stay curious, keep learning, and never stop questioning — because critical thinking is QA’s greatest superpower.

Stay tuned and stay curious — QA Insights from a QA Lead