The top 9 AI testing tools (and what you should know)

Software and quality assurance teams use AI in all parts of the automated testing workflow. 

According to a survey of 625 software developers we ran, 81% teams use AI tooling in their testing workflows for some variety of test planning, test management, test writing, and even analyzing test results.

But AI can make the biggest impact on the most time-consuming steps in the automated testing process: test creation and maintenance.

AI testing solutions generally take one of two approaches to creating and maintaining automated tests with AI: (1) AI-assisted test creation and maintenance (2) autonomous AI testing.

In this piece, we’ll dive into the pros and cons of each approach, sharing examples in 9 of the best AI test automation tools.

• AI-assisted test creation and maintenance tools
  • Rainforest QA
  • OpenText
  • Harness
  • Autify
  • TestRigor
  • Reflect
• Autonomous AI testing tools
  • Meticulous
  • ProdPerfect
  • Functionize

AI-assisted test creation and maintenance tools

Test creation with AI

Creating automated test scripts with AI prompts is so common across testing tools today, it’s basically a table stakes feature. 

Most of the tools work like this: you enter a plain-English description of the steps you’d like the test to take, and generative AI translates your prompt into a test script that the test platform or framework you’re using can execute.

Using AI to create test steps is particularly helpful if you’re using open-source testing frameworks like Selenium, Cypress, or Playwright. These frameworks are notorious for being a time-consuming pain to use, because someone with the right technical skills has to dig around in code to get anything done.

Test maintenance with AI

Less common — and more helpful in improving the velocity of the automated testing workflow — is AI that can help your team handle test maintenance, the (necessary) act of updating tests to reflect the latest state of your software application.

Without test maintenance, your tests will quickly become obsolete as you update and improve your app. Specifically, your automated tests will increasingly return false-positive test failures because they’re looking for a version of your app that no longer exists. When this happens, your software team will lose confidence in the test suite, and will therefore invest less energy in its upkeep. It’s a vicious cycle. 

Test maintenance is the most time-consuming part of the test automation process, particularly if you’re using an open-source framework. In fact, automated test maintenance is frequently a bottleneck in the code release process. So anything that can speed it up is a boon for software teams.

In some AI test automation tools, the AI can detect when you’ve made a minor, intended change to your app (like changing the label of a “Sign in” button to “Log in”), and then update the relevant tests accordingly. 

When this “self healing” AI technology is successful, the AI helps your team completely avoid time-consuming human intervention to keep your tests up to date (i.e., maintained). 

The drawbacks of AI-assisted test creation and maintenance

As helpful as AI can be in test creation and maintenance, it pays to have realistic expectations — AI has certain drawbacks.

1. AI isn’t ready to take over all test maintenance

If your app’s UI or functionality undergoes anything other than minor changes, AI likely won’t be able to update or self-heal your tests for you. It doesn’t have deep context that a developer or a product manager has to know the new, intended functionality that should be tested.

So, for the time being, the headache of test maintenance remains a human-powered task. That means teams working with open-source testing frameworks will continue to need to pay for expensive headcount to manage automated test suites. 

Either you’ll need to have your developers create and maintain your functional tests (which they’ll hate, and will distract them from their first priority: shipping code) or you’ll need to pay QA engineers with the right technical skills.

For context, an experienced QA engineer in the U.S. expects a salary over 100K.

2. You can’t fully trust AI outputs

AI in the context of testing tools is a lot like coding assistants like Copiliot — it can help speed up the process, but it hallucinates, so you need to check its output. 

Yet again, human intervention is required. In this case, it’s to make sure the AI is designing your test coverage in a way that protects your app’s critical user flows.

3. AI doesn’t save time for teams using open source 

Our developer survey also revealed that teams using open-source frameworks like Selenium, Cypress, or Playwright actually spend more time on test creation and maintenance when they use AI.

This could be for any number of reasons. For example, maybe AI is helping speed up the process, so these QA teams are taking on even more tasks. Or maybe open source teams haven’t figured out the most efficient ways to use AI, yet. 

Either way, automated test creation and maintenance are still time-consuming activities for human-powered teams. In fact, 55% of software teams using open-source frameworks spend more than 20 hours per week on test creation and maintenance, even with AI.

1. Rainforest QA

Rainforest QA is an AI-accelerated test automation service that includes an all-in-one, no-code testing platform. Rainforest is optimized specifically for end-to-end testing of web applications (not native mobile apps).

Rainforest’s platform includes everything you need for the testing workflow.

• Test management
• Cloud-based infrastructure for running tests massively in parallel
• Detailed test results including video recordings
• Integrations with any CI/CD platform, Slack, Microsoft Teams, and JIRA

Not only has Rainforest implemented AI for test creation and self-healing, but uses AI in other ways that speed up the testing workflow and minimize test maintenance slowdowns.

Get a quick, 45-second overview of Rainforest’s AI-powered self-healing capabilities in this video. (Or check out an in-depth, 6-minute demo.)

Rainforest uses a patent-pending AI approach for higher reliability 

Unlike other AI testing tools that use off-the-shelf implementations of LLMs like ChatGPT, Rainforest’s AI uses a patent-pending approach that improves its overall accuracy and reliability. 

Rainforest avoids test brittleness with AI 

Automated tests that frequently fail due to minor changes in your app have a name: “brittle tests.” Brittle tests, which are common with open-source frameworks like Selenium, require annoyingly frequent investigations and maintenance.

Tests in Rainforest are less brittle because they rely on three different types of identifiers to locate elements in your web application. These include visual appearance, an automatically-identified DOM locator, and an element description automatically generated by our AI. 

A change in any one of these identifiers won’t break your test. So the need for any maintenance — human-powered or AI-powered — is completely avoided.

Rainforest uses no-code for up to 3x faster test coverage compared to using open source

If you’re looking into using AI for testing, you’re likely interested in creating as much velocity in your software development life cycle as possible.

Rainforest has been designed from the ground up to maximize your velocity. That includes an intuitive, no-code test automation framework in which anyone from your team can quickly interpret or even update your tests without any training. 

Our survey data show that using Rainforest’s AI-accelerated, no-code platform is up to 3x faster than using open-source frameworks to create and maintain automated tests.

You can see the difference for yourself in this video, which shows the same test being created side-by-side in Rainforest and in Playwright:

Humans in the loop to check the AI’s work (and take test maintenance completely off your plate)

As we’ve established, AI isn’t yet in a place to save your team from having to perform time-consuming test maintenance.

That’s why every Rainforest customer gets a dedicated Test Manager who uses our AI-accelerated platform to take test creation and maintenance completely off your team’s plate. Plus, they check all the AI’s output so you don’t have to.

All of our Test Managers have been with us since 2017, undergo regular training and evaluations, and have been highly reviewed by other customers. They work in your time zone, speak fluent English, and embed in your comms tools like Slack and JIRA so they can deeply learn your product and priorities. Our customers say they feel like part of the team.

With Rainforest, you can enjoy the benefits of AI-accelerated test coverage without having to distract your developers from shipping code or having to hire expensive QA engineers to manage your test suite.  

And our plans start at less than a quarter of the cost of hiring an experienced QA engineer.

2. OpenText

OpenText’s Functional Testing tool features AI that can help with both test creation and maintenance for web and mobile applications.

Like Rainforest, OpenText’s test automation interprets the visual layer of your app like a human would. So you can have more confidence that it’s testing the true user experience, not just the behind-the-scenes code (like open-source frameworks do).

OpenText has a large product portfolio (mostly unrelated to testing) and is mostly aimed at enterprise customers.

3. Harness

Harness’s AI also offers both test creation and self-healing capabilities.

Harness offers products that cover many aspects of DevOps and the software delivery process, so they’re not specifically focused on AI-powered testing tools.

In addition to Enterprise plans, they offer “open source” and free plans.

4. Autify

With Autify’s no-code platform, you can perform cross-browser and regression testing. The Autify AI assists with test creation and maintenance. 

Parallel testing is only available on their Enterprise plan.

5. TestRigor

TestRigor has a long history of allowing users to create automated tests using plain-English prompts. Now their AI also supports self-healing. 

TestRigor supports testing of web, mobile, and desktop applications.

6. Reflect

Reflect was acquired by Smartbear in 2024. 

You can use plain-language prompts to create test steps one at a time, but their AI-powered test maintenance is limited compared to other tools. 

They also offer parallel testing in their cloud infrastructure, but for an additional price.

Autonomous AI testing tools

Autonomous AI testing tools create and update tests with minimal human intervention. They autonomously learn your app and create test coverage. 

These AI testing tools represent the ultimate promise of AI in QA — even if they don’t quite deliver on it. 

While generative AI tools know about a lot of things in general, they don’t have as much context as you do about your app. So, at least with the current state of the art, you can’t just “set it and forget it” with these tools — they still require human oversight to make sure there’s accurate test coverage for all the important user flows.

The CEO of testing tool Jetify acknowledges as much in this TechCrunch article about its autonomous AI, Testpilot:

7. Meticulous

Meticulous watches how you use your application while you’re developing it, and it keeps track of which parts of your code are being used during these interactions. From this information, it automatically creates tests that check if your application looks and works correctly.

8. ProdPerfect

ProdPerfect monitors and analyzes actual user behaviors in your app and automatically creates end-to-end functional tests that mirror the most common and important user flows.

9. Functionize

Functionize is similar to Meticulous in that it monitors how you use your app to autonomously create test coverage. But it also offers a low-code tool with self-healing that you can use to create automated tests.