Testing in AI era

Testing in the AI Era – New Reality for QA Teams

A few years ago, software testing was mostly about checking buttons, APIs, and writing regression automation. Today, the situation is changing very fast because AI is entering almost every part of software development. We are not only testing applications anymore. We are also testing AI-generated code, AI workflows, and AI models themselves.

This creates completely new challenges for QA engineers, automation specialists, and project managers.

Vibe-Coding Needs Coherence Testing

Recently, many developers started using "vibe-coding." Instead of writing every line manually, they describe an idea to AI tools like OpenAI ChatGPT, Anthropic Claude, or coding assistants from GitHub. AI generates large amounts of code very fast. Sometimes whole features are created in hours instead of days.

This speed is amazing, but also dangerous.

AI-generated code tends to look correct while hiding logical problems underneath. It handles the happy path well, but error cases and security edge cases — exactly the scenarios that matter most in production — are often skipped or handled superficially. The result is unstable architecture that duplicates whatever patterns happened to dominate the training data, good or bad.

Recently, I reviewed a pull request generated almost fully by AI. The code was working, the tests were green, and the demo looked nice. But after one hour of exploratory testing, we found that error handling existed only on the surface — every exception was caught and silently ignored. The application never crashed, but it also never told the user when something failed. This is exactly the type of problem AI-generated code creates: it works until it doesn't, and you discover it too late.

Because of this, testing also must change. We cannot only validate whether an application "works." We need to validate whether the generated solution makes sense in the long term.

This is where coherence testing starts.

Coherence testing means QA engineers go beyond checking outputs. They ask whether the feature behaves naturally for real users, whether AI-generated logic stays consistent across the codebase, and whether the system fails in a safe and visible way when something goes wrong. Most importantly, they watch whether code quality remains stable after dozens or hundreds of AI-generated changes pile up on top of each other.

Very often, manual exploratory testing becomes very important again. Human intuition still notices weird behaviors faster than AI tools.

Automation Testing in the AI World

Automation is also entering a new era. Traditional Selenium scripts are still important, but they are not enough anymore.

Modern AI-driven applications change the UI dynamically, generate content on the fly, and adapt behavior based on prompts or user context. Standard locators and predictable flows become fragile.

This creates demand for smarter automation. Teams are experimenting with self-healing scripts that repair broken locators on their own, AI-generated test cases that fill in missing scenarios, prompt-based test execution, visual AI validations, and even autonomous testing agents that explore an application without a predefined script. Tools using AI can automatically repair broken locators, generate missing test scenarios, or detect unusual behaviors from logs and screenshots.

But here is the danger nobody talks about: scale without ownership. When AI generates hundreds of tests per week, very soon the team has thousands of tests that nobody understands later.

The pipeline is still green, but nobody can say what is actually verified. A self-healing locator quietly "heals" itself into the wrong element and continues passing for months. The problem is not the quality of a single AI-generated test — the problem is what happens when you have ten thousand of them and no human ever reads the code.

The best results happen when AI speeds up repetitive work but humans still own the strategy and quality decisions. Automation should focus on real risk areas instead of being generated everywhere just because it can be, and exploratory testing should never disappear from the process. AI should support testers, not replace critical thinking.

AI should support testers, not replace critical thinking.

Testing AI Models Is a Completely Different Game

So far, everything covered here still builds on familiar ground — you have an application, and you test it. But what happens when the thing you are shipping is the AI model?

Testing a classic web application and testing an AI model are two different universes. Traditional software gives deterministic results: same input, same output. AI models do not work like this. The same prompt can return a slightly different answer every time — sometimes correct, sometimes hallucinated, biased, or unsafe.

That shift changes everything: the methods, the team structure, the definition of "done," and even the ethics involved. Prompt testing, hallucination detection, bias validation, prompt injection — these are not extensions of what QA teams already do. They are a separate discipline.

But that is a story for next time.

Future of Testing

Testing in the AI era is becoming more strategic than ever before.

Simple manual regression testing will probably continue disappearing. But strong QA engineers who understand systems, risk, automation, and AI behavior will become even more valuable.

Future testers will need a much wider mix of skills than before. Classic automation experience is still the foundation, but on top of that they need a real understanding of how AI systems behave, some fluency in prompt engineering, and the analytical and exploratory mindset to know where to look when something feels off. Data analysis and security awareness round out the picture — testers will increasingly be the people asking whether a model is leaking information or behaving differently for different user groups.

The companies that combine AI speed with a human quality mindset will deliver software faster and safer than competitors.

AI will not kill testing.

Actually, AI makes professional testing more important than ever before.