AI test automation only works when your product already has structured test cases, UI layouts, and user paths. That’s because AI needs clear, repeatable steps to create and run reliable tests.
But most growing startups don’t have dedicated QA experts to figure that out. Instead of saving time, AI tools create brittle tests that slow down your devs.
At Rainforest QA, we’ve built generative AI into our no-code platform.
Our features help you avoid the time-consuming and annoying work of keeping automated test suites up to date, so your software development team can stay focused on shipping fast.
Understanding the role of generative AI for test automation
Generative AI isn’t a substitute for your testing strategy, but it can make your testing faster and less repetitive.
Generative AI in test automation acts as a QA assistant that handles the grunt work, like writing test steps, healing broken tests after UI changes, and spotting flaky test logic. This allows your team to focus on shipping faster without compromising on quality.
Many startups don’t have a dedicated QA team to design a test plan internally, but expect AI testing tools to run tests accurately.
When generative AI has clear instructions, structured user flows, and consistent UI patterns, it can produce reliable results.
It also takes care of repetitive QA tasks like updating test steps and managing selectors, so your team can focus on edge cases, improving user experience, and shipping with confidence.
Why does most AI test automation fall short?
Most generative AI tools for test automation are easy to use, but they break easily. Real QA rarely comes on a silver platter. Here’s why most AI test automation fails:
- It expects a stable test framework. Many AI tools assume you’ve already built frameworks for different test cases. Without it, you need to define everything from scratch, which slows down your team.
- It breaks easily during small changes. Complex flows, dynamic UIs, or anything outside a happy path can throw off the AI. GenAI can’t handle the messy edge cases that come up in everyday QA.
- It relies too much on the code. Many AI tools test code behind the UI and ignore the visual layer, which actually matters to user experience.
- It requires prompt engineering. Tools like ChatGPT only work well if you feed them exact instructions. That means more time crafting prompts, not less time spent on QA.
- It creates maintenance overhead. When AI-generated tests break, your team still has to investigate, debug, and rewrite, often without visibility into what went wrong.
In the video below, our CEO, Fred Stevens-Smith, walks through what some of the generative AI features look like in action.
If you prefer to read instead of watching the video, we’ve detailed below the various ways we’re using generative AI for test automation. Plus, we’ve provided a short explainer on how we optimize our AI tool for software testing and how it’s different from other AI solutions in the QA market.
Rainforest’s AI test automation built for speed and reliability
Rainforest QA is a software testing service for growing SaaS startups. Our no-code platform combines generative AI and expert oversight to deliver automated testing that runs on virtual machines (VMs).
This means our platform tests what users actually see, helping you catch real bugs instead of wasting time on false positives.
We combine this with generative AI, which is trained on years of QA experience. Unlike basic GenAI tools, Rainforest AI runs context-aware and reliable test steps without breaking easily.
Here’s how Rainforest QA speeds up test automation using generative AI:
Create automated tests in plain English to ship faster
Many generative AI tools allow you to create automated test steps using plain-English prompts, which makes them ideal for writing test steps faster.
But in many cases, you need to write a separate prompt for every test step the AI needs to execute. For example, for your web app’s signup flow, you’d have to write step-by-step instructions with prompts like these:
- Click on the Sign Up button
- Fill the email field with tester@test.com
- Fill the password field with 12345
- Click the Continue button
That’s more manual effort than it should be.
We’re focused on helping you save time and move faster in your testing workflow. With Rainforest, you can create a whole series of steps with just a single prompt.
In the test scenario above, you could simply enter the prompt “Create an account using dummy data,” and Rainforest’s generative AI model would create comprehensive test coverage for your signup flow. This helps your devs move faster with confidence.
AI-powered self-healing tests that reduce maintenance time
Engineering leaders consistently tell us that maintaining automated test scripts is a time-consuming and tedious part of the testing process for their teams. It distracts them from their primary goal: shipping code.
We use generative AI to shift the burden of repetitive test maintenance from your team to our specialized AI agents.
When a test fails during execution due to an intended change in your app (and not due to a bug), Rainforest’s artificial intelligence will automatically update, or “heal” the relevant test steps to reflect your intended changes.
This helps your devs move faster and deliver quality tests that don’t break easily when you make small UI changes.
If a failing step comes from an AI-generated test based on one of your prompts, the AI will proactively update and save the test steps since it understands your intent.
For failing test steps that were created manually with no-code (and not with a prompt), the AI will suggest a fix for you to approve or deny.
This AI-powered self-healing functionality means you’ll spend a lot less time investigating and addressing false-positive test results stemming from intended changes to your app.
When Rainforest reports test failures, it’ll be more likely to represent actual bugs or potential issues you’ll want to fix to keep your app high-quality.
Rainforest helps you find the ideal balance between helping your software development team move quickly while giving you confidence in your test suite and software quality.
Any changes the AI makes are completely transparent. You have final control over your tests. The system also records version histories of the tests if you ever want to revert.
Avoid flaky AI test automation with reliable element locators
Test automation with generative AI can break when it relies heavily on code-based selectors. Even small UI changes can cause tests to fail.
Rainforest uses generative AI to create fallback methods for finding the app elements, like buttons and form fields.
A test automation tool uses a single method (like a DOM selector) to locate elements in your app, which leads to brittle tests that fail easily due to minor changes that aren’t apparent to users.
For example, if a button’s ID changes from “signup-button” to “sign-up-button,” a test looking for the “signup-button” ID would fail, even if the button’s appearance and functionality hadn’t changed. Someone would need to diagnose the test failure, identify the underlying issue, and update the test to use the correct element ID. It’s not rocket science, but it definitely interrupts workflows and is a low-value use of time.
Rainforest uses up to three different methods to identify elements in your app, which makes its tests a lot more robust.
These three methods include screenshots, DOM selectors, and AI-generated descriptions. When you or the AI first identify a target element by taking a screenshot, the system automatically captures the element’s DOM selector and generates a description of the element.
During test execution, if the system can’t locate an element based on its screenshot or DOM selector, the AI will search for the element based on the element’s description (e.g., “Pricing” located near the top middle of the screen).
Having these fallbacks means avoiding brittle tests that interrupt your team with false-positive test failures every time you make minor changes to your app.
Dedicated test managers who review every change to keep your devs in flow
Many software teams hesitate to trust generative AI to run tests on its own, and for good reason. Generative AI for test automation can help you move fast, but it doesn’t always understand the context of what matters to your users.
That’s why Rainforest’s test automation services include two dedicated Test Managers for your account. They embed into your team’s tools and workflows and work in your time zone, just like an internal hire. This streamlines your entire test management process.
Every time our AI generates or self-heals a test, your Test Managers step in to review the change. They confirm intent, catch edge cases, and make sure the test reflects user experience.
If a test fails and looks like a real issue, your test managers log it in your communication channel (like Slack) with full context, screenshots, and reproduction steps. That way, your devs don’t waste time on false alarms or searching for QA bugs themselves.
AI handles the busywork—your Test Managers decide what matters. That balance is what makes our test results fast, relevant, and trustworthy.
What makes Rainforest’s generative AI for test automation different?
Many developers are skeptical of generative AI tools because of unreliable output.
We’ve trained Rainforest’s AI with several unique methodologies to make it more reliable and well-suited for software testing.
Complementary agents can work out the big picture and the details
It’s difficult to teach a single AI agent to adopt multiple approaches. This is a common challenge in artificial intelligence. For example, it’s difficult for an agent to be both good at broad, high-level planning and narrow, detailed execution.
Instead of relying on one model to do everything, we’ve built AI agents that specialize in different tasks and give feedback to each other. During their collaboration, when they disagree, they start talking to each other iteratively until they reach an optimal decision.
This improves test quality and reliability, so your team can release with confidence.
You can read about our novel (patent-pending) “complementary agents” approach in this blog post.
Multi-modal data for testing the actual user experience
Rainforest’s AI agents use different types of data, including visual data, from the app under test to make decisions.
This approach follows the same philosophy that led us to focus on testing the visual layer in our no-code automation platform instead of the DOM (the behind-the-scenes code). Tests evaluate what users will experience, not what computers see in the code behind the experience.
That’s why our visual-processing algorithms have been trained using machine learning to simulate human judgment. When visual changes in the app under test are so small that a human software tester wouldn’t notice or care, the system will ignore those changes. (Though you can toggle on a “strict matching” option.)
This way, your tests only fail when the user experience actually changes, reducing unnecessary noise.
The AI works inside and outside the browser
Unlike other generative AI test automation tools, Rainforest’s AI isn’t limited to testing what’s in the browser window. It can evaluate and interact with anything you see on the screen in one of our Windows or macOS virtual machines, like the start menu, task bar, or file explorer.
While we’ve optimized the platform to test web apps, it can accommodate testing other types of software products related to web apps, like browser extensions and downloadable files.
This allows your team to test real-world user flows end-to-end, including edge cases that most AI testing tools can’t handle.
Benefits of using generative AI in test automation
AI can be a great assistant for repetitive QA work. Here are the benefits of using AI in test automation:
Faster test creation
AI helps you turn user flows or requirements into test steps automatically, which is useful for common scenarios like login, signup, or checkout.
Reduced maintenance
AI can auto-heal broken selectors or adjust scripts to match new layouts. You don’t need to update tests manually after UI changes.
Smarter issue detection
AI can flag flaky tests, false positives, or redundant checks, so your team spends less time reviewing test failures and more time fixing real issues.
Continuous coverage
AI can run tests across multiple environments or browsers at scale, helping you spot issues that only show up in specific conditions.
Natural language inputs
Some AI tools let you describe tests in plain English, lowering the barrier for non-technical team members to contribute to test coverage. But this doesn’t mean you can replace QA experts entirely.
Bottom line: Ship faster with AI test automation that doesn’t break
Rainforest QA helps you automate tests without the usual pain of flaky scripts or constant upkeep. With self-healing tests, visual validation, and expert review built in, your team can stay focused on shipping, not chasing false positives.
Book a live demo with our team to see how Rainforest supports test automation using generative AI, so your team can ship fast with confidence.
If you’re a growing SaaS startup ready to automate your manual testing efforts and level-up your testing strategy, talk to us about checking out a live demo of our AI testing tool.
