How to evaluate and choose the right autonomous testing tool for your team

Could you get by without a convenient and intuitive wallet app? Or let’s take it from another side. You are trying to get into shape and need your fitness app to analyze your goals, parameters, nutrition habits, etc, and build a clear-cut plan. The takeout — modern software is becoming more and more complex. That’s why software testing also needs to transform.

And at the moment, there is no better solution than autonomous software testing. The global AI testing market could reach almost USD 846 by the end of 2026.

Trailblazers have shown that AI can halve testing cycle times without sacrificing software quality. Spotify, Airbnb, and other famous companies choose machines to test software autonomously. Interesting point: while we see that big companies try to monopolize AI solutions, startups across the world are changing the parity.

That’s why we crafted this article — to save you time, money, and competitiveness. Answering how to choose autonomous testing tools, we will cover key criteria, common challenges, and practical tips.

Why autonomous testing tools are essential

Speed of testing matters

Every industry evolves, so testing is just a reflection of software’s rapid growth in complexity. In the past, manual testing was the norm, but there is a new norm now.

Manual testing was much longer and often brought about delayed releases. For example, manual regression testing took several days or even weeks.

And here we are now — faster cycles, faster releases, faster improvements. Continuous integration brought about the appearance of agile methodologies, DevOps practices, and autonomous testing solutions.

They settled down due to their capability to:

• Less manual load: It automates repetitive tasks and, thus, allows QA teams to focus on more complex testing scenarios.

• Wider coverage: The more tests —the bigger the coverage, the more environments. Since the test suites are not static, you also cover more edge cases and critical functionalities.

• Enable continuous testing: AI-powered testing tools, once integrated into CI/CD pipelines, run tests automatically as a natural part of the flow.

Autonomous testing vs. traditional automated testing

This table is incomplete because autonomous testing has many more features and advantages. For example, scalability and long-term financial benefits.

Key criteria for evaluating autonomous testing tools

How difficult to learn the ropes

User-friendly interface, easy management, adequate learning curve… Try to strike a balance when selecting a new testing tool. A complex one will frustrate the team, and a too-childish one won’t meet your expectations.

• Interface: An intuitive one simplifies navigation and the effort spent on training. A well-designed dashboard improves usability and facilitates quicker adaptation.

• Documentation: Tutorials, user guides, FAQs, etc. significantly ease the onboarding.

• Customer support: Responsiveness of customer support is the base of your reputation. Access to timely assistance is critical when teams face challenges.

Features and nuances

This is ABC in testing tool selection. When we choose a car we consider abilities, control, and safety first. The AI tool is a machine too. But there is a thing — you should take into account possible feature scaling and growth. However, must-have features are constant, give or take:

• Self-reliant test generation: Next-gen tools automatically generate test cases and thus increase test coverage and save you time.

• Ongoing maintenance: Self-healing is when the tool adapts to application changes and thus reduces maintenance burden.

• Real-time analytics: We are used to post-factum analytics. However, in important projects, the ability to assess test performance and identify failure patterns in real time may save you much more time and money than you can even imagine.

• Diverse testing types: Unit, integration, functional, and regression testing — flexibility ensures that the tool covers the full testing life cycle.

• Scalability: You grow, the app grows, and so does the AI testing tool. That’s simple.

Compatibility with your tech stack

If we asked you about AI testing adopters, what company would you name first? Amazon, Google? Or maybe SpaceX? How about eBay? When the e-commerce star adopted autonomous testing, they started off ensuring compatibility with their diverse tech stack (Java, Node.js), and the CI/CD pipeline.

We all want our online shopping to be as smooth as possible and some more. That’s what guided eBay’s process for choosing an autonomous testing tool.

How to evaluate tool compatibility

• Review supported platforms: Always check the official list of supported programming languages, frameworks, and platforms. Don’t limit yourself to the current stack (forecast future changes), but ensure that you can put the tool in your existing workflow.

• Check integrations: Jenkins, Travis CI, test management systems (e.g., Jira, TestRail) — look for features like API access or pre-built plugins that make the integration effortless.

• Ask the development team: Devs and QA engineers should cooperate as a cohesive team. Different perspectives and understanding of the current flow will guide you to tools that will fit.

• Assess cross-platform functionality: If your application spans multiple platforms (web, mobile, etc.), ensure the testing tool can accommodate this diversity.

• Test compatibility in a sandbox: If possible, run a trial version of the tool in a controlled environment to determine how it interacts with your current setup before making a full commitment.

Cost and ROI

Total cost of ownership (TCO) is an essential metric that includes all expenses on the tool. Namely:

• Licensing fees: Upfront costs and any recurring subscription fees.

• Setup costs: Consider expenses related to implementation: configuration, integration, and any potential training costs for the team.

• Ongoing maintenance: Money and time for maintaining the tool, updating it, or support services.

When calculating ROI, take into account:

• Reduced testing time: Automation saves time. Faster testing cycles = quicker release schedules = increased productivity. Remember it.

• Improved quality: Fewer defects in production, lower bug fixes, customer support load, and rework costs and consequently improve customer satisfaction (which is the most important metric, actually).

Vendor reputation and support

From simple actions to a deep dive:

1. Check into customer reviews (feedback, tone, overall satisfaction level, hints on potential challenges associated with the tool).

2. Investigate the vendor’s standing within the industry (accolades, certifications, commitment to quality proof, etc.).

3. Ensure the vendor’s support team is responsive and operates in your time zone. They should also have several channels: chat, email, phone, etc.

4. Ask the tech community for case studies. Forums, user groups, media branches, or even comments — use any accessible platform that can shed some light on the tool’s performance.

Steps to evaluate and choose the right tool

Step 1: Define your requirements

• Create a checklist of must-have features and desired outcomes:

• The new tool should seamlessly cooperate with the existing ones

• All testing types that we need are supported (unit, integration, end-to-end)

• AI test optimization

• Reporting and analytics features [in real time as an option]

Involve key stakeholders – developers, testers, and QA managers – in the requirement-gathering process. Use the RACI framework to clarify roles and responsibilities.

Step 2: Shortlist potential tools

Weed out tools and create a shortlist. How to:

• Online reviews (For example, G2)

• Industry webinars and conferences

• Peer recommendations

Suppose you’ll have at least three to five tools to compare features, pricing, and user experiences. That’s fine, but try to keep it under five.

Step 3: Trial and demo

Several years ago, Forrester conducted research to find out how trials influence decision-making. Despite there being no reliable data as of 2024 (some sources state it’s a 70% improvement, but this is a lie), one thing is clear — trials bring about practical insights via tangible experience. As an option, request a demo.

How to fathom key features out

1. Test with your existing CI/CD pipeline.

2. Measure the time taken for various test scenarios.

3. Compare results against known outcomes.

Step 4: Assess scalability and future-proofing

Can the tool grow with your team’s needs and support future technology changes? Check if the tool updates regularly, adds new features, and stays competitive. Look for:

• Roadmaps for future features

• Community engagement and support

• Historical performance in adapting to new technologies

Step 5: Gather feedback and make a decision

Involve the broader team in the final decision-making process. Gather feedback from those who participated in trials or demos. Organize different thoughts into a clear-cut memo, map of thoughts, or visualization, summarizing all insights.

Tips

• When conducting a demo, record the meeting if possible and then use AI to summarize key features.

• When gathering feedback, use AI tools (again!) to identify key ideas that could impact your project.

• Create a pros and cons list for each tool, considering both technical capabilities and overall team fit.

• Size up the options collectively, ensuring alignment with team goals.

Common pitfalls to avoid

Ignore integration complexity

Underestimating the time and effort required to integrate the novelty into existing systems is a killer. So there is nothing groundbreaking in unexpected delays and increased costs. To avoid disruptions to your workflow, check the ease of integration. Here is how.

How to suss out integration complexity

• System audit: Conduct a thorough review of your current technology stack, including programming languages, frameworks, CI/CD systems, and test management tools. This will inform you of potential compatibility issues.

• Check documentation: Review the tool’s documentation for detailed information. Look for specifics regarding API availability, supported integrations, and setup procedures.

• Request a POC: Ask the vendor for a proof of concept that demonstrates how the tool integrates with your existing systems.

• Engage with technical support: Work with the vendor’s technical support team to understand the integration processes clearly. Their insights will help you estimate the complexity of integration.

• Plan for migration: Map out the migration; include key steps, required data, configurations, and responsible people.

Ignoring user experience

Features are important, but your choice shouldn’t be limited to them. User experience can also have detrimental effects on tool adoption. Robust functionality with a steep learning curve may demotivate employees.

Unintuitive interface and inadequate usability increase training time and defeat the purpose of revising the software testing strategy.

User experience check

• Is the interface easy-to-perceive: Ask for a demo. Assess the intuitiveness of the user interface, its layout, and potential difficulties with the onboarding.

• Gather user feedback: Bring team members into the evaluation process to gather their opinions.

• Look for training resources: Tutorials, quick reference guides, troubleshooting videos — a comprehensive learning system will ease the transition.

Focusing solely on cost

The cheapest option is not always the best — the miser pays twice. The long-term value and ROI of new tool adoption can offset the initial cost.

AI-powered testing tools yield dividends in efficiency and effectiveness, from streamlining testing processes to reducing defect rates. Fewer bugs = better software quality = less money spent on re-work, debugging, and compensation for unsatisfied users.

Long-term output calculation example [for a medium app]

Input:

Size of the app: medium (E-commerce platforms, SaaS tools, etc.)

Releases per month: 4

Previous option: in-house QA with USD 90,000 a year

Calculation for OwlityAI:

Needed plan: Pro

Total costs are about USD 1,311 a month

Total time saved: 428 hours a month

Cost savings: USD 6,189 a month

Bottom line

Hope you now understand that testing tool selection is more about the ability to comprehend all the factors that influence effectiveness and net costs.

This article tips you on how to choose autonomous testing tools. When calculating the total cost of ownership, include time savings, increased productivity, and decrease in bugs in production.

If you are still on the fence, contact our team, and we will figure out how we can help. Feeling confident? Start off by hitting the trial button below.