As a preface, we believe itās not just artificial intelligence features that you should consider when exploring enterprise AI testing tools. If you pick a tool with sufficient AI functionality yet lacking in general QA, you will have to work in at least two tools. Integrations with your other tool is a major consideration as well.
aqua
AI features: Test case generation, test steps, test description, test prioritisation, duplicate removal
aqua is the most mature solution on the list. Launched in 2013, it is an Application Lifecycle Management tool with a heavy emphasis on quality assurance functionality. The tool is developed by andagon GmbH, a German vendor and IT consultant with over 20 years of experience.
Benefits
- aqua’s AI testing features cut through the complexity for enterprise QA teams. Unlike typical solutions that overwhelm users, aqua boosts productivity without the headache of extensive training. Start by implementing its risk-based prioritisation feature to immediately focus testing efforts where they’ll deliver maximum value.
- aqua lets you tune the level of AI involvement. If youāre ready to live on the bleeding edge, aqua can generate entire test cases for you from plain text. This works well, but often still requires human tweak before adding the test into your suite.
Alternatively, you can let aqua speed up the routine while doing the bulk of the job. aqua can auto-create test steps based on description or do the opposite ā auto-create description based on test steps. It can even do the chores, i.e. remove duplicate test cases or prioritise tests within your test suite.Ā - aqua AI understands your project. If you look into ChatGPTās capabilities, you know it can generate code reasonably well. There is one major problem: ChatGPT does not know your project. aqua also uses the machine learning model behind ChatGPT, but that model is applied to your code and existing tests. This is what makes aqua AIās output personalised and thus relevant.
- aqua is an otherwise feature-rich product. It is an Application Lifecycle Management tool that can do what some teams use five tools for. aqua is superb at quality assurance, but it is also great for any software development and project management needs. 12+ native integrations and REST API pave the way for great synergy with third-party software.Ā
Disadvantages
- aqua is not among low-code tools for test automation. While itās another way for organisations to do more with fewer resources, aqua does not offer a simplification layer for test automation.Ā
- aqua is the latest tool to launch AI functionality on the list. The solutions below have been working on artificial intelligence for longer, even if that doesnāt always result in better or vaster functionality.
See why aqua AI is disrupting the market
ACCELQ
AI features: Test case generation, self-healing tests
ACCELQ is a major player among codeless AI testing tools for enterprise companies. It lowers the entry barrier for test automation for both aspiring QA specialists and business stakeholders. The American entity behind ACCELQ was founded in 2014.
Benefits
- ACCELQ has dedicated tools for different domains. The company offers solutions for web, API, and mobile testing automation. Manual testing can be enhanced with yet another separate solution or alongside automated tests in ACCELQ Unified.
- ACCELQ delivers no-code testing that works across React, AngularJS and other major frameworks. Its AI capabilities let you create tests using natural language while automatic element identification and self-healing execution keep things running when your app changes. Try starting with model-based testing for end-to-end flows and cut test maintenance by nearly half while improving coverage across complex enterprise environments.
- ACCELQ brings a business-driven approach to quality assurance. It integrates with Salesforce to automatically create self-healing tests. It provides a lot of templates to cover common use cases with automated tests. It streamlines testing between iOS and Android so youāre verifying the general mobile experience rather than two different solutions.
Disadvantages
- Usage quirks and some performance deficiencies are widely reported by users. Some are frustrated with failing tests when using multiple automation agents. Others reported that there is still no functionality to move work between projects as of late 2022.
- Documentation is another frequent concern. Despite a lot of recent effort, documentation requirements are simply higher for tools that target people with little tech experience. Luckily, the knowledge centre is public and you can see if it is enough for you to handle the trial version of ACCELQ solutions.Ā
Testim
AI features: Test case generation, record and playback testing, self-healing tests
Testim is a leader in AI testing for user interface testing. It was boosted by Microsoftās accelerator programme in 2016 and recently acquired by the test automation giant Tricentis.
Benefits
- Testim learns from your application. The toolās artificial intelligence studies patterns and individual decisions made during implementation. Unlike aqua, Testim uses this information to maintain tests rather than create them.
- Testim emphasises stability. It comes with self-healing tests, error aggregation and even solution ideas for problems, and network mocking to decouple troubleshooting from third-party services.Ā
- Testim is highly scalable. It can run the same tests on different browsers, and that naturally includes emulating different resolutions of various devices. Youāre free to use Testimās own grids, employ standardised Selenium-compatible grids, or even go for something third-party.
On a similar note, Testim integrates extremely well with other QA and software development tools. Apart from common Jira/Slack/Jenkins integrations, Testim can exchange data even with competitors like Applitools.
Disadvantages
- App performance reportedly can degrade with a large test case. This is counterintuitive to why you would use a test automation tool, so do check if thatās a problem with Testimās free trial.
- Pricing gets unpredictable, especially early after adoption. You pay for test grids rather than licences, and it can be hard to tell how many you will need. This is even more of a problem if youāre new to AI-assisted UI test automation or automated QA in general.
What tool to choose?
There is no definitive answer, and this article doesnāt try to do an awkward apples-to-oranges comparison just to pick āa winnerā. All three tools serve a distinctive purpose:
- aqua is a rapidly developing solution that uses AI to save you time on QA across the board
- ACCELQ targets no-code teams to make software testing as seamless as modern no-code development can get
- Testim adds the AI flavour to the highly competitive niche of UI test automation tools
Sorry to sound bland when talking about exciting AI tech, but you do need to pick the tool that fits your needs best.
Best Practices for Adopting AI Testing in the Enterprise
Getting AI testing right requires smart groundwork and not flashy implementations. Kick things off with a focused pilot project to test compatibility and create internal advocates who’ll champion the technology throughout your org. Connect your AI tools with existing CI/CD pipelines and tracking systems. This integration cuts friction by nearly half in most deployments we’ve tracked.
Even with ‘no-code’ platforms, don’t skip training. Your teams need to know how to steer AI outputs in the right direction. One quick tip: start by having your QA team document their manual test creation process before automating it. This creates a perfect blueprint for AI guidance.
Remember, AI doesn’t replace human judgment. Think of self-healing tests as accelerators, not replacements. Have your QA pros review AI-generated tests, especially for critical workflows where bugs could cost you big. Track your results against pre-established baselines and adjust your approach as both the AI tech and your testing needs evolve.
Conclusion
AI test automation got more attention thanks to ChatGPT, but it existed long before it. There are several niches that match a lot of use cases. Unlike in some markets, it is trivial to find an AI-powered solution from established vendors.
AI Testing in Action: Practical Enterprise Examples
AI-powered testing is already delivering major wins at top companies. Instead of running thousands of regression tests, they identify critical subsets in minutes ā matching tests to actual code and business changes.Ā
Look at how e-commerce teams are ditching manual UI test fixes. Their tests now self-heal when frontend updates happen ā cutting maintenance from hours to just minutes while catching bugs earlier. Pretty slick, right? Banking teams are taking a different approach ā they’re actually generating tests automatically from app models. This frees up their QA pros to tackle the weird edge cases machines still struggle with.Ā
These companies are working at a totally different speed and scale now. Start small ā pick one test maintenance bottleneck in your process and apply AI there first. You’ll see fast improvements without needing to overhaul everything at once.
Use fine-tuned and personalised algorithm behind ChatGPT to tackle your individual QA challenges
