What do we mean by ‘test automation frameworks’?
Engineers often use the term to refer to the core open-source automation layer they write code against – Cypress, Playwright, or Selenium, for example.
We think that this definition is a little restrictive. In this day and age, where you have a range of AI-native options that offer huge advantages over traditional testing platforms, why restrict yourself to discussing functionality that is largely outdated?
In this article, we consider anything that provides the structure for test automation – commercial platform or open source solution – a test automation framework.
Here are our tips for choosing the right one – and a few suggestions to kick off your shortlist.
How to Choose the Right Test Automation Framework: 7 Top Tips
1. Prioritize Maintainability and Scalability
The best automation frameworks are designed to grow with your product. Look for:
- Affordability at scale: If costs skyrocket beyond what’s proportionate as soon as your test suite starts expanding, look elsewhere
- Modular architecture: Tests should be easy to update without changing large parts of the codebase
- Maintenance requirements: Your test automation framework shouldn’t need an explosion of maintenance hours as you start to scale
- Separation of concerns: Your framework should keep test logic, UI element definitions, environment configuration, and test data separate, so that updating one area is less likely to break another.
2. Look for Low-Code Options for Efficiency
Low-code testing allows non-engineers to contribute to testing workloads – and, if your engineers are testing their own code, it will save them a ton of time. You have a few options here:
- Visual test creation using drag-and-drop building blocks
- Natural language scripting, where tests can be written in plain English
- AI-enhanced record-and-playback, so the recorder generates stable and optimal locators
- Automatic script generation from requirements, user flows, or Gherkin steps
If you’re looking for ultimate efficiency, natural language test creation is the way to go (though we’ll give record-and-playback an honorable second place). Ultimately though, it’s all about what your team is most comfortable with – get them to demo a few tools to see what works.
Also worth noting: low-code tools should still allow custom code, so that you can ensure the test automation framework tests your most complex cases in the precise way you’re comfortable with.
3. Opt for Tools Offering AI-Enabled Self-Healing Tests
Self-healing reduces flakiness and speeds up maintenance – especially in fast-moving agile or CI/CD environments where the UI changes frequently.
Traditional UI automation has a tendency to break whenever element locators change. Self-healing helps the framework automatically detect changes in the UI and adjust the locator strategy on the fly. Look for frameworks that:
- Automatically identify the correct element when primary selectors break
- Offer explainability, showing how and why a healing decision was made
- Log alternative locators or offer patches that can be approved by the tester
4. Choose Tools With Agentic AI
Think of AI agents as autonomous virtual colleagues. Instead of automating tasks when prompted, AI testing agents can plan, execute, revise, and maintain test suites on their own.
Agentic AI has the potential to transform how you do QA – you’ll spend less time on routine maintenance and more on higher-value strategy and analysis work. Look for AI agents that:
- Generate tests from requirements or user journeys
- Run exploratory testing to discover bugs by interacting with your app as a user would
- Optimize your test suite by removing duplicates and prioritizing tests based on risk
- Fix broken tests by applying patches or updating outdated logic
- Can run in CI pipelines, making independent decisions like retrying flaky tests
5. Ensure Strong Integration With Your Tech Stack
These days, you don’t need to spend weeks manually integrating new test automation frameworks with your existing tools and tech stack. Anything that’s good will be plug-and-play with most of the following:
- CI/CD systems (GitHub Actions, Jenkins, GitLab CI, Azure DevOps)
- Test management tools (Jira, TestRail, Zephyr)
- Code repositories and version control workflows
- Cloud device platforms and containerized environments
- Reporting and observability tools
6. Check Your Tool’s Reporting, Analytics, and Insights
Strong reporting features allow you to both identify defects quickly and pick up on trends in testing data, helping you improve key processes over time. Look for:
- Rich HTML or dashboard-style reports
- Screenshots, videos, and DOM dumps on failure
- Root-cause analysis powered by AI
- Trend analysis for flakiness, coverage, and stability
7. Look at Reviews, Referrals, and Testimonials Before Committing
Do your research. Whether you’re looking at an open source framework or a commercial platform, look for opinions from real users with experience using the tool. You could:
- Ask vendors for case studies and testimonials (don’t be afraid to follow up with the client themselves if you have further questions)
- Ask your professional network for recommendations
- Check out user forums for open source tools
- Explore software review sites like Capterra or industry reports like Gartner’s Magic Quadrant
Read case studies for Momentic
The Best Test Automation Frameworks: Our Top 5
1. Best AI Test Automation Framework: Momentic
Want to really leverage the power of AI? Opt for a test automation framework that’s been completely built around the technology, rather than one that tacks it on as an afterthought.
Momentic is an AI-first testing platform that offers:
- Natural language low-code testing – explain what you want the test to do in plain English, and the AI will make it happen
- Self-healing features that actually work, with intent-based locators and smart waits that minimize flakiness and slash your team’s maintenance workload
- AI agents that explore your app, find critical user flows, and generate/update tests autonomously
Don’t just take our word for it – ask our clients, who have 4x’ed their release cadence, sped up their daily test executions 14x, and expanded their test suite to 80% coverage without writing a single line of code.
2. Best Open Source Test Automation Framework: Playwright
If you’re on a budget or are happy to spend time customizing an open source tool, Playwright is our top pick. Here’s why:
- Out-of-box AI agents for efficiency – no fiddly plugins required
- It’s an established framework – your engineers have probably used it already, and there’s a mature community for troubleshooting online
- Cross-browser and cross-platform compatibility – you can test Chromium, Webkit, or Firefox, and across Windows, Chrome Linux, and macOS
- A range of neat features, including Codegen for recording tests (and saving them in any language), and mobile web emulation for Android and Mobile Safari
Playwright isn’t code-free, so some level of scripting knowledge is required. It’s also important to note that it’s for web apps only, so it isn’t an option for native mobile teams.
3. Best Test Automation Framework for LLMs: Deepcheeks
Are you integrating LLM functionality into your apps at the moment? Are you planning to over the next five years? Chances are, the answer to one of these questions is ‘yes’.
LLM functionality should be tested and verified like any other app features – DeepCheeks is one of the most widely used LLM testing frameworks, and offers:
- Bias detection/mitigation, to pinpoint biases in responses at scale
- Performance monitoring, to track LLM performance and identify drift issues early
- Customizable benchmarks to tailor evaluations to your organization’s important metrics
- Visual dashboards for at-a-glance insights into key metrics
4. Best Test Automation Framework for Real Device Testing: SauceLabs Real Device Cloud
If you’re sold on lots of real device testing (and can work around the cost/scalability issues that this often throws up), SauceLabs offers thousands of device/browser/OS combos to test your app across, and can be deployed as a cloud tool, behind private networks, or on-premise.
Features include:
- Device-specific debugging, including logs, screenshots, and performance data
- AI test creation agents for low-code testing
- Built-in accessibility scanning for web apps
- Support for a wide range of frameworks, including Selenium, Playwright, and Cypress
5. Best Test Automation Framework for Visual Testing: Eggplant
Eggplant was ahead of its time as a testing tool – it’s been using AI to help engineers shift their visual testing workloads efficiently and accurately for over a decade.
Eggplant’s AI compares expected images of interactions with real-world user interaction results. Other key features include:
- Cross-browser/platform/device support, with the ability to test web, mobile, and even legacy/remote systems
- Extensive performance testing features for scalability
- AI exploratory testing to explore different user paths, detect anomalies, and cover scenarios that scripted tests might miss
- Low-code testing via record-and-playback
Momentic: The Test Automation Framework That Can Save 40 Engineering Hours Per Month
"It’s like giving someone your QA checklist and watching them execute it for you"
Sriram Sundarraj (Engineering Lead, Retool)
Using Momentic’s AI features, our clients Retool were able to save 40 engineer hours per month and 4x their release cadence to 4 times per week. Pretty handy for a platform used by over half the Fortune 500.
Want to join them? Book a demo today
