Smarter testing with AI | Best AI testing tools for automation and accuracy

What happens when a routine update works in staging but causes issues for users on specific devices or browsers after release? Faster release velocity makes it more challenging to detect such problems without robust validation processes.

Testing approaches that adapt to rapid change help teams release faster, manage complex integrations, and maintain consistent quality across devices and environments. At Global App Testing (GAT), we’ve seen teams that rely solely on traditional automation face last-minute firefighting, missed cross-platform issues, and delayed releases. 

Delivering consistent quality at speed requires strategies that go beyond scripted checks and focus on maintaining stable tests, prioritising high-risk areas, and keeping coverage aligned with frequent product changes. 

In this blog, we will examine how  AI testing tools and techniques can help the QA and engineering teams reduce test time, ensure devices and platforms are consistently covered, and improve software quality.

What is AI testing, and why does it matter?

AI testing uses machine-learning–driven techniques to support and automate key parts of the software testing lifecycle, from test creation through execution, maintenance, and analysis. Unlike traditional automation, it adapts to application changes and improves over time through testing feedback.

For QA and engineering teams, AI testing helps to:

• Catch defects sooner in the software lifecycle
• Validate user experiences across multiple devices
• Reduce manual testing effort without compromising scope
• Maintain reliable releases as applications change

Moreover, AI testing complements experienced QA teams when used effectively, by reducing routine maintenance and enabling greater focus on high-impact scenarios that influence user experience and build confidence.

How is AI changing test automation?

In our experience, AI-based testing reduces the burden of constant script updates. Tools like GitHub Copilot can generate scripts, manage locators, and adapt to UI changes, keeping tests stable and reliable.

In practice, this allows QA teams to:

• Spend less time maintaining scripts
• Decrease failures caused by minor UI changes
• Expand test coverage across platforms and environments
• Focus on higher-value scenarios and exploratory testing

As automation becomes easier to maintain and scale, QA and engineering leaders naturally begin to evaluate its impact. Let’s explore the tangible benefits these tools deliver: speed, reliability, and more innovative use of resources.

In our experience with customers, understanding this difference allows our QA teams to plan testing efficiently, ensuring fixes work while maintaining overall system stability. 

What are the benefits of using AI-powered test tools?

Testing tools boost efficiency and reduce maintenance by adapting to application changes, helping teams maintain a stable environment as products evolve.

Benefits of Test Tools

For QA and engineering teams, these tools help to:

• Speed up regression and functional testing by updating test coverage as features evolve, keeping releases on schedule.
• Minimize failures from UI changes by maintaining reliable tests even when interfaces or elements are updated.
• Reduce maintenance and rework by limiting frequent script changes and simplifying test upkeep.
• Improve coverage of high-risk areas by focusing tests on frequently changing or business-critical features.
• Effectively allocate quality assurance time across releases by identifying regions where changes are most likely to occur.

Artificial intelligence-based testing tools can help identify potential bugs in a program and make test creation easier by analyzing test results and execution patterns.

How do AI testing tools handle test creation and maintenance?

Testing tools like Testim, Mabl, and Functionize make it easier to create tests by letting teams define scenarios in plain language, such as login flows or key user journeys. The tools then generate end-to-end tests, reducing manual scripting and effort. 

To minimise ongoing upkeep, these tools can:

• Monitor UI and DOM changes and update locators automatically
• Reduce flaky tests caused by minor layout or styling updates
• Suggest test adjustments based on application changes
• Lower long-term maintenance effort for QA teams

For instance, Global App Testing’s QA teams introduced self-healing tests for a media client, cutting test time by 50% and enabling broader coverage with less maintenance and faster deployments. 

With test creation and maintenance becoming easier, the next challenge is scaling how teams can expand reliable coverage across web and mobile applications.

Can AI accelerate test coverage for web and mobile applications?

An AI-driven tool now supports web and mobile coverage by simulating various devices, browsers, and OSes. Automated test exploration and script writing help to easily cover missing areas and thus ensure quality.

Cross-Platform Test Coverage

Our teams have observed the following benefits of these tools in improving test coverage and efficiency:

• Intelligent crawling to discover all app flows and pages
• Cross-browser and cross-device validation for consistent behavior
• Faster end-to-end testing across environments
• More efficient use of QA resources to focus on critical scenarios

Improving test coverage reliability and scalability allows teams to thoroughly test each release without adding manual effort. This expanded coverage also sets the stage for deeper functional and exploratory testing, where understanding real user behavior becomes critical.

Once functional behavior is verified, visual checks ensure the application’s look and feel remain consistent across devices and updates.

What is the role of AI in functional and exploratory testing?

Apart from scripted testing, the use of AI-based tools such as Testim, Mabl, and Functionize for functional testing, and Applitools or Percy for exploratory testing,  helps in understanding the user interface and how users engage with the product, which might not be identified through regular testing processes. 

AI Testing Workflow Overview

From our experience at Global App Testing, these tools contribute to testing efforts in several key ways:

• Automating regression and edge-case tests to reduce manual effort
• Generating test cases based on historical workflows and user behavior
• Recognizing areas lacking coverage and suggesting additional test cases
• Enhancing the quality and completeness of test suites

Teams gain confidence that their applications perform correctly in real-world conditions. They also apply the same rigor to UI and visual testing to ensure a consistent and accurate user experience.

How does AI improve UI and visual testing?

The user interface (UI) and visual testing tools, such as Applitools, Percy, and TestMU AI, enable QA teams to identify discrepancies that manual testing misses. These tools enable teams to run UI tests automatically, allowing them to verify changes more quickly, while manual testing focuses on key areas.

TestMU AI Visual Comparison

With these UI tools in hand, it is necessary to shift towards selecting the appropriate automation tools for testing. They should be intelligent, integrated, and scalable to produce high-quality results for testing web and mobile applications.

What are the best AI test automation tools?

Selecting quality test automation tools helps scale quality assurance and enable reliable coverage. Good test automation tools offer functionality, integration, and innovative testing tools for web and mobile apps.

At Global App Testing, we often see teams rely on tools such as:

• Testim (AI-driven functional testing): Offers innovative locator strategies and adaptive test creation.
  • Testim helped an enterprise team implement self-healing test scripts, reducing maintenance overhead and improving stability.

• Mabl (AI-driven functional testing): Automates workflow adjustments using test data.
  • Mabl enabled a SaaS team to automate complex workflow tests, cutting regression testing time in half.

• Applitools (Visual/UI testing): Advanced visual testing for UI consistency and layout validation.
  • Using Applitools, a global finance platform automated UI validation, eliminating manual review and increasing confidence in releases.

• Functionize (AI-driven testing): Simplifies test creation and maintenance using natural language.
  • Functionize helped an enterprise team generate and maintain end-to-end tests, reducing manual scripting and improving test coverage.

• Postman (API automation): Streamlines API testing with automated request execution, validation, and environment management.
  • Postman helped a fintech team automate API tests across multiple environments, reducing manual effort and improving integration reliability.

At Global App Testing, teams often scale test automation by pairing automated checks with crowd-driven testing across real devices and environments. While tools handle repeatable validation, GAT’s global tester network provides practical coverage across languages, locations, and usage patterns. This combination helps teams maintain release confidence while expanding coverage beyond what in-house automation can realistically support. 

Teams achieve results by integrating tools into daily testing workflows, supporting ongoing validation, and aligning them with QA priorities. Selecting tools alone isn’t enough.

How do teams integrate AI into their testing workflows?

Testing works best when integrated directly into development workflows. Teams can run tests after every commit and catch issues as code changes by embedding automated testing into CI/CD pipelines.

CI/CD Testing Workflow

At Global App Testing, we recommend these key integration practices:

• CI/CD alignment: Run tests automatically on every build to catch issues early.
• Coverage optimization: Identify gaps in test planning and prioritize high-risk areas.
• Test case recommendations: Add relevant test scenarios based on past results and recurring patterns.
• Performance monitoring: Fine-tune infrastructure and resource usage to keep testing fast and reliable.
  
  

GAT insight: In practice, Global App Testing has seen the strongest results when engineering leaders pair automation-driven insights with expert human review. This balanced approach ensures generated tests remain aligned with functional requirements and security standards, while still scaling efficiently across releases.

Once testing is embedded, it makes sense to plan, leveraging stronger tools, broader use cases, and growing adoption to evolve automation.

What’s next for AI testing in software development?

Testing automation is advancing rapidly, enabling faster software delivery and stronger quality. Engineering and QA teams can look forward to:

• Natural language test authoring: Write test scenarios in plain language, making test creation quicker and easier for teams to understand.
• Self-healing test scripts: Tests adjust smoothly to UI or workflow changes, reducing the need for constant updates.
• Smarter API and integration testing: Target the highest-risk areas by drawing on system architecture and real-world usage.
• Expanded test management: Apply informed judgment to assign resources appropriately to satisfy testing priorities.
• End-to-end automation pipelines: Comprehensive test cycle management with reduced manual intervention, catering to swift and successful release procedures

Being up to date with these variations lets teams have confidence in adopting evolving testing practices. The following takeaways spotlight making advanced testing part of the routine workflow.

Key takeaways

Effective QA starts with selecting the right tools and embedding testing into workflows. Focusing on critical features and efficient resource use ensures reliable releases.

The teams also optimize test suites and make Data-driven releases based on test results and trends. Although automation speeds up testing, skilled testers are responsible for accuracy, regulatory compliance, and quality. Agile strategies, including scripts, API testing, and end-to-end testing, enable teams to keep up with shifting application needs.

Take the guesswork out of QA. With Global App Testing, you can deliver reliable software faster and with confidence. Explore our services today.