Self-healing tests became the QA industry's biggest bet in 2025. Vendors claim AI can fix broken automation overnight. Engineering teams are burning budgets on tools that supposedly eliminate maintenance overhead. But the marketing hides a messier reality: self-healing is real, just not the way most people think. We spoke with Dmytro Kyiashko, a Software Developer in Test specializing in AI systems testing, to find out what actually works when tests block a deployment.
TechTimes: Dmytro, everyone's talking about self-healing tests. But when you're actually building test frameworks for AI systems, how do you define this concept?
The term gets thrown around so loosely that it's almost lost meaning. Magic has nothing to do with it. This is about intelligent adaptation within defined boundaries.
In practical terms? A self-healing test detects when a breaking change isn't actually breaking the functionality—just the way we're accessing it. The DOM structure changed, the selector broke, but the button still does what it's supposed to do. The test should be smart enough to find it using alternative strategies without human intervention.
The non-negotiable part: it must log what it did. Transparency matters.
So, you're saying most companies misunderstand this?
Completely. The biggest misconception is that self-healing means you can write tests once and forget about them forever. That's not engineering—that's wishful thinking.
I've seen teams implement self-healing frameworks and then act surprised when their test suite becomes a black box nobody understands. Two months later, they're getting false positives because the tests "healed" themselves into testing the wrong things entirely.
The illusion the market sells: "AI will fix everything automatically, so we won't need to allocate time for refactoring and maintenance." That's worse than wrong—it's reckless.
What does real self-healing look like in your work?
We built a healing mechanism using multiple identification strategies—CSS selectors, visual positioning, text content, ARIA labels, element relationships. When the primary locator failed, the system attempted alternatives, validated the element still performed the expected action, and flagged the change for review.
What didn't work? Letting AI make healing decisions without context. Early iterations would latch onto completely wrong elements that happened to match some criteria.
Self-healing without guardrails creates chaos.
How do you balance automation with human oversight?
This is where my experience building engineering teams from scratch really matters. Through nearly ten years in IT—over four of them leading teams and more than six years building engineering operations, conducting technical interviews, and mentoring specialists—I've seen what happens when companies try to remove humans from the equation entirely.
The solution isn't human versus algorithm. It's a collaboration.
The engineer's role fundamentally shifts. Instead of manually fixing every broken test, you're setting the rules, defining the boundaries, and making judgment calls when the system encounters ambiguity. You become more of an architect than a maintenance worker.
He has already written a practical guide that breaks down a step-by-step methodology for building evaluation frameworks for AI systems. The central idea is clear: humans decide what "correct" looks like, and AI applies that logic at scale. The book explores how this principle translates into real-world results—higher accuracy, faster feedback loops, and evaluation systems that actually support the development of better models.
Technology can't replace intuition—not yet, maybe not ever for certain types of problems. When you're testing multimodal AI systems, which I've published research about, you need someone who understands context, user intent, and business logic. An algorithm can detect that something changed. Only a human can determine if that change matters.
As an IEEE member surrounded by cutting-edge research, where do you see this heading?
In five years, nobody will call it "self-healing" anymore. The concept will be so embedded in test engineering that it won't need a special name.
The bigger shift? The line between AI development and QA will blur significantly. Testing AI with AI requires a different mindset. Traditional testing assumes deterministic behavior—same input, same output. But modern AI systems are probabilistic by nature.
I've been working on frameworks for automated evaluation of AI systems precisely because this is where the industry is heading. As an independent consultant with Xenoss—recognized by Inc. 5000 as one of America's fastest-growing private companies and named AI Company of the Year at the 2025 UK Business Tech Awards—I'm building solutions for automating tests of agentic AI applications.
When I'm reviewing papers for international conferences like the World Conference on Emerging Science, Innovation, and Policy, I'm seeing incredible research into adaptive testing methodologies.
My bet: self-healing won't become standard because it's magical. Teams will adopt it once they understand its limitations and use it appropriately.
What should companies focus on right now?
Stop chasing the buzzword. Start asking better questions.
When someone sells you a "self-healing test framework," ask: What decisions does it make autonomously? How does it handle ambiguity? Can you audit its healing actions? What happens when it heals incorrectly?
The best QA automation doesn't eliminate human involvement. It eliminates tedious work so humans can focus on complex problem-solving.
At events like UAtech Venture Night during Web Summit Vancouver, where I've evaluated startups pitching to international investors, I thoroughly examined the processes of each team presenting their startups at this meetup, assessed the technical aspects of each product, and dove deep into their engineering workflows. Not a single team could free their engineers, including Quality Engineers, from the maintenance and support of automated tests, even with the use of cutting-edge AI agents and frameworks.
I also work with one of the leading technology companies in the U.S., consistently recognized by Fortune and Fast Company for innovation and workplace excellence. You don't get that by replacing people with AI. You get it by giving them better tools.
Any final thoughts for teams implementing this technology?
Self-healing works. But it's engineering, not magic.
Build transparency into your system. Log every healing action. Create approval workflows. Set clear boundaries for what your tests can and cannot adapt to automatically.
Most critical: keep a human in the loop. AI is capable. But quality needs judgment, and judgment needs humans.
The hype says AI will do everything. The reality? AI will do a lot if you build it right and understand exactly what problem you're solving.
Self-healing tests scale human expertise. They don't replace it.
Dmytro Kyiashko is a Software Developer in Test with nearly 10 years of IT experience, specializing in AI systems testing. He has built engineering teams from scratch, conducted hundreds of technical interviews, and mentored junior specialists throughout his career. His work focuses on ensuring the reliability, stability, and performance of AI-powered solutions at scale.
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