These days, whenever you scroll through your feed, you’ll mostly see people talking about AI helping with coding.
Whether it’s an “AI pair programmer,” “AI generating functions,” or even “AI fixing bugs,” the spotlight is all on coding.
But if you look closer, hardly anyone talks about AI in testing.
And yet, testing is one of the most critical stages of software development—consuming just as much time and resources as writing the code itself.
Making AI Smarter Together with Chain of Context >> Read more
1. From Manual Testing → Automation Testing
Back in the day, testers had to click through every single flow manually—time-consuming and error-prone.
Then came Automation Testing tools like Selenium, JUnit, and Cypress. These allowed us to run test cases automatically, reduce repetitive tasks, and perform regression testing much faster.
But the problem? Test scripts are extremely fragile. A small UI change could break everything, forcing constant fixes. Instead of being a time-saver, automation often became a burden. Unless you had a dedicated resource for it, teams would eventually push it aside because there were always “more urgent tasks” at hand.
2. From Automation → AI-Generated Test Cases
This is where AI steps in.
- AI can automatically generate test cases from requirements or even from the code itself.
- With computer vision, it can recognize UI elements far more intelligently than traditional locators.
- It can adapt test scripts to survive UI changes—without requiring manual fixes every single time.
3. The Common Pitfall: Testing in Silos
Traditionally, testing has often been done in silos—at the unit, module, or feature level.
But as systems grow, real-world impacts span across multiple components. Manual + automation testing often fails to cover these cross-functional risks.
AI helps here by connecting the dots. For example, it can predict that if Flow A breaks, Flows B and C will likely be impacted too—reducing blind spots that teams usually miss.
4. However… AI Is Not Magic
AI doesn’t magically know how to test right out of the box.
If no one teaches it or feeds it with the right context, tests will still “miss the mark.”
AI needs to understand the business logic, what’s critical, and what the edge cases are. In the end, humans must train AI with the framework of the system to ensure meaningful, high-quality testing.
Interesting AI Testing Solutions
To be honest, I haven’t tried them all myself, but based on research and reviews, here are a few worth noting:
- Testim (by Tricentis) → Uses AI to maintain test scripts automatically, focusing on Web, App, and Salesforce testing.
- Mabl → A platform for regression and visual regression testing powered by ML, claiming over 95% coverage.
- Functionize → Enterprise-focused AI testing. Lets you write test cases in natural language, which the system then generates into tests.
- Copilot for Testing → Many teams now use GitHub Copilot to help generate unit tests and integration tests.
Takeaway
Automation testing used to be the hero—but today, it might be time to rethink.
AI not only reduces repetitive tasks but also gives us better visibility into system-wide impacts, increasing confidence in quality.
But remember: AI needs guidance. Without context or defined logic, it will fail just like traditional testing.
That means testers must go beyond traditional testing skills. They must learn how to teach AI, so AI can help test effectively.
Automation testing used to be the hero—but today, it might be time to rethink.
AI not only reduces repetitive tasks but also gives us better visibility into system-wide impacts, increasing confidence in quality.
But remember: AI needs guidance. Without context or defined logic, it will fail just like traditional testing.
That means testers must go beyond traditional testing skills. They must learn how to teach AI, so AI can help test effectively.
See you in the next PRIMO Tech-a-Break.