Low-Code Test Automation Tools: What to Look for (and What to Avoid)

Whilst speeding up test creation is very useful, a good low-code test automation tool needs to work at scale if you want to maintain efficiency benefits long term. 

With AI tools like Momentic, you don’t have to choose between ‘simple’ and ‘powerful’. AI does both, allowing you to expand your coverage seamlessly, test in more depth, and run complex scenarios just as easily as your most basic unit tests. 

Here are four essential features to look for in low-code test automation tools to maximize efficiency, scale effectively, and release more robust code.  

1. Look For: Low-Code Test Automation Via Natural Language Processing

A good codeless testing tool should be so quick and easy to use that your engineers can test more of their own code with next to no extra time input. This has the potential to completely revolutionize how you run your testing, because: 

• You shift your testing left to run concurrently with development. This catches bugs when they are easier and cheaper to fix, accelerating releases and improving overall code quality. 
• You lose the inefficiencies and bottlenecks associated with a separate QA team. You pass less code ‘over the wall’ (or none at all) and speed up your release cycle.
• Your engineers own the code they create, improving code quality and reducing the potential for technical debt. 
• You save on QA resources, and can reinvest this where it really matters – better engineers, better tooling, infrastructure upgrades – the choice is yours. 

If you want to maximize these benefits, you need the simplest low-code tool possible – and what could be simpler than testing without code by typing what you want the test to do in plain English, then letting AI take care of the rest? 

For teams really looking to light a fire under their release schedules, natural language processing is a must. Type in what you want the test to do, then let the AI build the test, run it, generate a report and identify bugs. That’s all there is to it. 

Avoid: Stopping At Rules Based Automation

Non AI low-code QA tools  aren’t fast enough to realize these benefits completely. You’ll get some of the way there, before tool complexity and time input become a barrier. 

Record and playback, drag and drop, or libraries consisting of prebuilt test components aren’t as efficient as natural language processing when it comes to automating your test suite. Tools relying solely on rules-based automation are lagging behind already, and won’t help you compete in the long term. 

And, (as anyone who’s ever used Zapier or other rules-based tools to try and automate a process will tell you), ‘low-code’ isn’t necessarily a synonym for ‘simple’. Any time spent creating tests or learning how a tool works chews into valuable development time – which is what you’re trying to avoid. 

2. Look For: Scalability Via Native Parallelization Features

Your low-code test automation tool should be able support you as you grow your test suite – both by remaining usable for more complex tests and by being able to help you expand your test coverage when needed. 

Finding a tool that offers native parallel test support is a good way to do this. As well as increasing your test coverage and speeding up execution, parallelization helps you test more extensive scenarios reliably and increases test depth.

Look for at least one of the following to make parallel test configuration as simple as possible: 

• Built-in orchestration: the ability to run multiple tests or test suites concurrently via internal thread or process management.
• Cloud scaling: the ability to spin up multiple containers, VMs, or real devices to execute tests in parallel, often auto-balancing workloads.
• AI intelligence: the ability to dynamically distribute tests, self-heal failures, and adjust concurrency based on test coverage or test flakiness.

For next-level parallelization, look for AI-led smart test execution. This uses a combination of historical data and ML change impact analysis to optimize which tests to run in parallel, and to only run tests relevant to a particular code change. 

Avoid: Complex Scalability Setups

Many tools claim to support parallel tests. The whispered part of that claim is that you’ll need to invest significant resources on complex setups that don’t guarantee reliability. 

For example, you can use Cypress Studio to generate tests without code via the record and playback method. Technically, you can run tests in parallel with Cypress too – with a lot of extra configuration and environment setup, and a risk that the tests become brittle in dynamic CI environments such as ephemeral containers. 

Do your homework on how parallelization works for different low-code test automation tools to make sure you’re choosing one that offers simplicity alongside scalability. 

3. Look For: Self Healing Tests and DOM Independence

Fast test creation is of limited use if you’re still plowing hours of engineer time into maintaining your test suite. That’s why AI features that reduce flakiness and enable auto healing are a must-have on any new automated testing tool you implement. 

Here’s how AI reduces the test maintenance burden so your bored engineers can go and do something more fun: 

• AI identifies elements based on descriptions and/or visual cues rather than traditional XPath or CSS selectors. This makes them DOM independent, so they won’t flake in the event of an update to the UI.  
• Configurable smart waiting features wait for the page to stabilize before performing test actions. This removes the need to add explicit ‘Wait’ steps to your tests manually – these are a common source of flakiness for pages with dynamic content.
• AI testing tools get smarter the more they test – they adapt to your applications and testing processes to reduce flakiness even further. 
• AI-generated tests can self heal using natural language locators, removing the need for lengthy manual maintenance runs. 

In other words, AI-generated tests are more reliable in the first place, and when they do flake, they self-heal. You get those hours of manual test maintenance back, remove slowdowns caused by brittle tests, and minimize false positives.

Avoid: Relying on Autowait Tools To Do The Job For You

Automated testing tools without AI do offer autowait features, but these can be clunky and difficult to use. This is because: 

• They often run on fixed or arbitrary timeouts, which are difficult to configure for real-world application performance. 
• They may rely on binary conditions, which can return true even if the element is partially obscured, mid-transition, or off-screen.
• They don’t intelligently learn from previous test runs, so you don’t get the efficiency benefits of an AI tool that constantly adapts based on previous test performance. 

4. Look For: Visual Regression Testing To Increase Test Depth

The right low-code test automation tool won’t just help you test more. It will help you delve into the detail, allowing you to test edge-case scenarios alongside the most common paths. Ultimately, this results in a more reliable, robust product. 

There’s no singular ‘test depth’ switch that will do this for you – good test depth results from a range of factors, including multi-platform support, how your low-code testing tool handles complex flows, and your approach to multi-layer testing. 

One feature that will help significantly, however, is the ability to incorporate visual regression testing into your testing processes. 

Screenshot comparisons are a fast and accurate way of validating UI consistency across varied conditions  – use them to deep test UI changes, responsiveness and layout variations across a range of edge cases. They offer two key benefits when run alongside your functional test suite: 

• You catch layout shifts, z-index overlays, font changes, or image misalignments that aren’t explicitly asserted in code.
• You catch UI layout changes that only appear in specific contexts by running visual tests across multiple devices and screens.

Advanced AI visual regression tools can also use AI to filter out unimportant changes, such as those caused by dynamic page elements (such as animation, ads, or time-based changes) to surface meaningful regressions only, and avoid overwhelming teams with false positives. 

Avoid: Relying on Third-Party Plugins For Visual Regression Testing

Third-party plugins for visual regression testing often lack analytics infrastructure, execute slowly due to lack of optimized cloud infrastructures, don’t integrate seamlessly with CI/CD pipelines, and experience issues testing dynamic content. 

Typically, you’ll need to manage your baseline images manually and update them whenever the design changes. Without the easy update UIs and versioning support offered by native tools, this is a time-consuming chore your team doesn’t need. 

Momentic: AI Low-Code Test Automation For Simplicity and Scale

Momentic is a low-code testing tool that offers test creation via natural language processing, self-healing tests to reduce time spent on maintenance, and intuitive parallelization options that cut down the configuration needed to the bare minimum. 

We help you boost the quality of your product code, whilst shipping faster than ever before. Looking to eliminate QA bottlenecks, identify defects early, and beat your competitors to market? That’s what we’re here for. 

Why not schedule a conversation with our team?