I Tried the Best AI Software Testing Tools: Find Out Which Ones Can Streamline Your QA Process

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1. Testim: Best for Fast-Authoring Tests

I started my review process with Testim. It’s an automated platform that streamlines end-to-end software testing for web and mobile applications. Right after signing up, it will already ask you about your goals to personalize the experience with the app, which is a nice touch for me.

Testim prides itself on its fast-authoring features. The codeless recording is a definite plus for quickly capturing user interactions because it proactively suggests reusable elements and structures your tests as you go.

I can see how this could help businesses cut down initial test creation time, especially for less technical testers. One of the features that got me hooked with Testim is the way AI is integrated in it. The software testing tool is continuously learning test patterns and solutions as you input them into the system through its auto-improving, or as they call it, self-healing locators.

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Instead of just reacting to changes, Testim recalculates, performs a side-by-side evaluation, and then automatically updates the locators to improve testing.

Testim’s result logs feature is also super helpful in QA because instead of just a “failed” message, you get screenshots showing what went wrong. Plus, it collects technical details automatically for each step. Imagine the hours I could spend digging through logs and finding errors when this feature exists — so personally I appreciate this!

If manual testing is your game, the platform enables the flexibility to run custom JavaScript along with its real JS editor, which is precisely the feature that makes Testim more adaptable and usable for more advanced testers.

Overall, I am really impressed with how the platform has simplified testing with its low code features. The interface is also straightforward, which is a plus, especially for non-technical quality management analysts.

As Alex Pickrell, Founder and CEO of Pickrell Global Technologies, highlights, Testim is an extremely powerful end-to-end testing tool that leverages AI to automatically update tests when CSS selectors change — substantially reducing the amount of maintenance typically associated with E2E tests.

"We're currently still evaluating Testim in our development workflows to expand our use of E2E tests," Pickrell adds, underscoring its growing role in streamlining modern development pipelines.

What Users Say

Most senior QA analysts and testers implement Testim in their workflows, commending the tool for its ease of use. Some teams, on the other hand, emphasize that the tool is okay, as long as the testers have JavaScript knowledge to better customize its scripts and basic testing knowledge.

Despite these, there are users who had negative experiences, citing Testim’s failing locators as well its lack of built-in, user-friendly mechanisms for validating multiple elements as a significant drawback.

Who’s It For?

QA teams and analysts who have worked with JavaScript in its test building can make the most of Testim’s customizable features. The tool can also be best maximized as a support to speed up a team’s testing workflow rather than using it as is.

Notable Features

• Generates automated tests using AI to quickly adapt to application changes
• Supports running tests across various browsers
• Continuously learns from test executions, improving locator accuracy
• Allows reusing custom code steps across tests reducing maintenance
• Integrates with popular tools across the development lifecycle, including GitLab, Jira, and GitHub

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2. Applitools: Best for Visual Testing

When one thinks of software testing, it usually comes off as highly technical — and it is. But with AI testing support like Applitools, quality analysts get access to full-service test automation with an emphasis on visual testing. At first glance, it can be overwhelming to look at, especially with all the features in the dashboard. But once you get the hang of it, testing is a breeze.

The first feature that you should set up is the Match Set because Applitool’s AI will refer to this as the standard match level when you run your test scripts. I find this extremely helpful and time efficient, especially when reviewing large sets of visual data.

Root Cause Analysis is another tool that I am particularly invested in. It provides users with an overview of the differences between the Baseline and Checkpoint materials and enables testers to investigate further on the bug and troubleshoot as necessary. Meaning, you don’t have to manually go through each error and analyze it — Applitools does it for you.

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Beyond its comprehensive visual testing features, Aplitools stands out from other solutions for implementing AI in every stage of the testing process. The flexibility of using a plain English authoring and integrating with existing coded frameworks offers a well-rounded approach to cater to different skill sets analyst teams have.

I tried this AI software testing tool without any major expectations, but Applitools delivered beyond my initial expectations. Although it does have a bit of a learning curve, its features are carefully thought of and are not just there as ornamental tools.

What Users Say

QA analysts praise this AI software testing tool, saying it’s a game changer in proactive visual testing.

Most users praise Applitools for the accuracy of its visual testing features and wide integration capabilities. One user even calls it the “industry leader.” However, the only apprehension that testers have about it is its pricing, as it is considered expensive compared to similar tools.

Who’s It For?

Applitools can be used by companies who have a design-heavy and critical interface to ensure a seamless user experience. From online marketplaces to financial institutions, visual testing tools like Applitools can help maintain an error-free frontend and automate the detection of UI defects and inconsistencies that might be missed by functional tests.

Notable Features:

• Its robust Visual AI can analyze visual elements and detect any anomalies accurately and in large batches
• Provides accessibility testing that ensures applications are compliant with legal and ethical standards
• Detailed insights into test failures by identifying the root cause

3. Mabl: Best for Non-Functional Testing

Mabl, a newcomer AI software testing tool, provides users with an easy-to-use platform that takes testing a step further. I was most impressed by its comprehensive capabilities to test the non-functional aspects of an application, web, or API, specifically its security, accessibility, and even content.

Mabl continued to wow me as I explored its script-free test creation feature. It is so easy to use that you can just run your application, navigate through its content and Mabl will start flagging bugs and errors through Mabl Trainer.

After spending some time with it, I can confidently say that the tool truly lives up to the low-code promise, allowing testers to generate functional UI tests simply by interacting with your application.

While the basics are intuitive, building truly complex test scenarios within Mabl’s Trainer can become counterproductive. I feel like it’s a bit inefficient when I try managing variables and conditional steps than writing code directly. That said, highly technical teams with very specific needs may see it as an unnecessary step rather than helping with their productivity.

Fortunately, what Mabl limits in complexity, it doesn’t compromise in flexibility. It allows JavaScript editing in the platform with the help of generative AI.

After taking a closer look at Mabl’s core features, I can see how leaner teams can benefit more from its no-code testing. However, it can be limiting for developers who are used to code their tests from scratch and have more complex software.

What Users Say

Testers who have tried Mabl mostly have neutral feedback on the tool. One user commented that they were caught off-guard by its cost, as it was relatively expensive, “and it’s not even all the bells and whistles.” A user also complained about its “weird” naming structure for their test cases. However, in my opinion, this may be an isolated case and will depend on the expertise of the tester using the platform.

On the other hand, some users took note of Mabl’s good documentation and responsive customer support, which was affirmed by other users. It was also comparably faster in terms of execution from other AI software testing tools, as emphasized by a tester.

Who’s It For?

QA teams with a mix of manual testers, automation engineers, and developers can benefit the most with Mabl’s low-code interface. While it makes test automation accessible to non-programmers, advanced developers can extend their capabilities with the tool’s JavaScript and Playwright integration.

Notable Features

• Simplified test creation through its low-code interface
• Easy management of automated tests for web and mobile applications without extensive coding knowledge
• Updates test scripts automatically in response to minor application changes
• Unified approach to testing by integrating various test types into a single workflow
• Offers scalability and flexibility to testing demands without extensive infrastructure management

4. Katalon: Best for Scalable Testing

Before testing Katalon, it has already been on my radar because of its popularity. But once I tried it, it truly is what was marketed — an all-in-one troubleshooting platform that eliminates the need for individual integration tools. While I’m not a big fan of the interface, and setting up the tool can be challenging for beginners, Katalon’s AI capabilities are undeniably top-notch.

Katalon maximizes generative AI to turn plain test instructions into test scripts. It can also be used by project managers to generate simplified explanations of the technicalities of the code to better communicate them to business leaders.

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Another feature that I like in Katalon is its proprietary AI called TrueTest. It bases test execution on actual user behavior, which adds a layer of test coverage and focuses efforts on the bottlenecks that web visitors experience.

While there are some drawbacks to consider, Katalon’s unified structure makes it a reliable solution for organizations looking to automate their software testing. For me, it’s definitely worth exploring for teams seeking a flexible quality management platform.

What Users Say

Many users had a hard time testing with Katalon, pinpointing its weaknesses towards scalability and maintenance. For better usage of the tool, some users think it would be better to name variables properly and apply POM as much as possible.

Who’s It For?

Quality assurance teams who already use Selenium in their workflows can find it easier to adapt to Katalon. Its user-friendly interface, complemented by its easy-to-use features, also makes the tool accessible for smaller teams with limited resources or specialized QA engineers.

Notable Features:

• Facilitates automated testing for software
• Dual scripting interface for users with limited coding experience and a script view for advanced testers
• Provides a comprehensive object repository for managing test objects
• Reusable keyword libraries

5. Functionize: Best for No-Code Testing

Functionize promises big: an autonomous testing tool powered by machine learning. As I explored its features, the platform’s automated workflow adaptation already caught my interest.

With an extensive archive of testing data, Functionize has created its recording plugin, Architect.

Architect collects data as it sifts through codes and domains to power Functionize’s machine learning model and understand the purpose of each test step, not just the specific actions performed.

Another thing that got me hooked was the tool’s Generative Creation feature. The idea is that I can just tell their enterprise application intelligent (EAI) agents what I want to automate using regular English — no coding involved at all.

What’s more interesting is that the user flows that the EAI agents write also consider live visitor data. My take on this is that if I could build tests based on real user journeys without having to manually recreate them, it would save a lot of resources.

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However, my top-of-mind concern for this feature is if they’re using live user data, I’d want to know how secure and private that data is. Or if the web visitors consented to use their personal information.

All in all, Functionize brings testing a step forward into an automated future. While that can expedite software lifecycles, there’s a part of me that really wants to understand more about how AI adjusts and builds my tests. But if Functionize can find the balance between automating while still giving me the necessary visibility and control, then it could really be a game-changer in how I, and larger development teams, approach QA.

What Users Say

While there are limited user sentiments on the tool, a user reported how Functionize has helped with its organization’s end-to-end testing. It particularly emphasizes the platform’s capability to self-healing and diagnostic features.

Who’s It For?

Organizations with manual testing workflows who are looking to transition to a fully automated test creation can adapt greatly with Functionize’s no-code interface. It can also be adapted by teams without extensive coding skills, where they can make use of the tool’s features like codeless record and playback features.

Notable Features:

• Visual testing
• Multi-language and multi-currency testing
• Geotargeted testing
• 24/7 dedicated support
• Self-healing tests that automatically adapt when UI elements change
• Smart test scheduling prioritizing tests based on the likelihood of issues recurring
• Automated test strategy generation based on historical data

Pickrell points out that one of the most common challenges when integrating AI tools into the testing process is simply figuring out which tools to use. "There is an abundance of different tools to try," he said, "and our recommendation is always to integrate one tool at a time, starting with the ones that will have the highest measurable positive impact on the business as a whole."

Pickrell gives a practical example, noting that if developers are currently writing all tests by hand, integrating an AI tool could "substantially reduce the amount of time spent by developers writing tests, which frees them up to focus on new feature development."

AI Software Testing Tools FAQs

1. How can AI be used in software testing?

AI is used in software testing to automate test creation through the help of generative AI and natural language processing (NLP) frameworks. Moreover, AI optimizes test execution by automating large tests datasets, reducing manual effort and improving efficiency. Through machine learning, AI can also predict bottlenecks based on user behavior and adapt to common changes in UI or application elements.

2. What types of tests can AI software testing tools automate?

AI software testing tools can accommodate a wide range of tests to automate. From functional testing that validates app behavior, regression testing that ensure new changes don’t introduce bugs, and performance testing to assess system responsiveness under stress, these tools are designed to support QA teams in improving their overall process.

AI tools can also expedite visual testing to detect UI inconsistencies and security testing to identify app vulnerabilities. As AI gets better and testing models adapt to its development, more aspects of software testing can be covered in the future with more accuracy and efficiency.

3. How do I choose the right AI software testing tool for my business?

Choosing the right AI software testing tools for your business requires you to analyze your priorities, the complexity of your product, and the type of testers that you have. Teams with a more holistic approach to testing and a less complicated product can benefit greatly from automated testing tools. But if you have complex software and a technically advanced QA team, it is best to find AI testing tools that offer flexibility for manual coding.