Even though it’s in the early development stages, there are significant benefits to using current AI technology. Introducing AI to a testing team builds experience that benefits the team as well as application customers. Imagine no more surprise defects popping up in production or hair-on-fire bug-fixing sessions.
AI-driven testing tools are transforming QA by streamlining test creation, reducing maintenance, and enhancing coverage. These tools give teams insight into requirements, identify critical test scenarios, and deepen their understanding of AI. Early adoption brings valuable experience and innovation, paving the way for proactive testing that minimizes surprise defects and last-minute fixes.
AI evolution requires continuous learning — teams familiar with using AI will find themselves ahead of the curve. This guide explores leveraging the benefits of AI technology to enhance test automation, examines current limitations, and suggests tools useful for building test intelligence.
Intelligent testing is experience combined with test development that incorporates AI and machine learning (ML) technology to improve testing quality and efficiency. Intelligent testing blends human expertise with advanced technology, enabling teams to achieve rapid, high-quality, and efficient test execution across increasingly complex, integrated systems.
Automated testing tools contain useful AI and ML technology that enables testers to create improved scripts. However, there are limitations that affect the quality of how AI operates.
Automated tools with AI are useful for:
Good news for every testing team, the following top rated test automation tools are useful for improving a team’s overall test automation efforts. Each tool includes a handy free trial for testing teams to ensure the tool works with existing infrastructure and test strategies. The list of tools is longer than included here. Based on our experience supporting test automation for clients, the top 5 tools for overall test automation:
Each tool supports a testing team's needs to improve test automation internally and for a greater impact on application quality for customers. For the tools selected, here’s a quick overview with pros and cons for each:
Tool |
Pros |
Cons |
| Katalon |
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| LambdaTest |
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| Tricentus Tosca |
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| Opentext UFT |
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| SmartBear TestComplete |
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The overall benefits of using AI for test automation include:
AI opens up a testing team's ability to accomplish more work without adding resources or overwhelming existing resources. AI technology enables testers to develop credible test automation suites within the sprint timeline while increasing test coverage. Testers using AI can build unit tests, add any number of edge case tests, analyze code patterns, and identify potential defects. Tests can also be executed in parallel to save time and reduce repetition.
Testing teams using AI can also learn to create performance, security, and visual test suites. Additional test coverage closes defect gaps and provides customers with thoroughly tested code for higher application quality. AI also expands the testing team’s reach and business value. Test teams must first integrate AI into test team processes. The benefits of integrating AI into a testing team’s strategy or process include:
AI provides significant and valuable benefits for testing teams. AI is well suited for improving automated test development’s cost, efficiency, and accuracy. It can expand automated test coverage, analyze requirements, and assist testers when creating scripts.
Automated test maintenance has plagued testing teams trying to automate effectively. With AI, test maintenance time is significantly reduced so that testing teams can use test automation to reduce repetitive work. Getting testers involved with AI helps build test intelligence into testing processes.