A Simple Guide to AI Testing Tools in 2024

AI is everywhere, and its influence on testing and QA is profound. The potential for AI-enhanced software testing tools to  revolutionize the industry is undeniable, transforming inefficiencies and bottlenecks as well as high-risk, low-quality releases into models of operational excellence. This technological leap won't just accelerate but also transform product delivery for the better. Our goal is to shed light on these advancements in this blog, as well as provide a reality check on what we should expect of AI tools today.

What are AI-augmented software testing tools?

AI-augmented software testing tools are defined by Gartner as technologies that enable continuous, self-optimizing, and adaptive automated testing. Unlike traditional tools that automate predefined tests, these advanced tools generate test cases and scenarios merely from requirements or user stories, offer iterative refinement, optimize test suites, and more.

Why adopt AI-augmented testing tools?

Today's tech landscape is evolving rapidly, and the world of software testing is trying hard to keep up. Modern applications are complex and many teams still rely heavily on manual testing and outdated tools.

This reliance leads to several challenges, including operational inefficiency and low reliability and stability of products.

“AI-augmented software-testing tools provide value through greater efficiency in the creation and maintenance of test assets and by aiding teams in optimizing test efforts, providing them with early feedback about the quality of release candidates," 2024 Gartner Market Guide for AI-Augmented Software Testing Tools

Outdated testing practices aren’t just slowing us down - they're also putting our ability to stay competitive and relevant in the market at risk. It's clear that as technology advances, our approaches to testing must evolve too.

One of the trends we’re seeing in digital organizations is increasing complexity of the digital IT landscape. Organizations today have huge webs of intertwined applications, both packaged and self-grown software, that are integrated and customized. If something breaks it can have a huge impact and cause domino-effect consequences.

But grasping these complexities and figuring out how to secure continuity is causing a huge cognitive load on the people managing them. And this is just one of many challenges that AI has the potential to solve.

According to Gartner's report, by 2027, 80% of enterprises will have integrated AI testing tools into their software engineering toolchain, a significant increase from just 15% in 2023. According to the 2024 State of Testing Report, 60% are not using AI today. So whilst adoption is still relatively low, it’s anticipated to pick up quite fast.

AI testing tools will be able to enhance developer productivity and product reliability by providing faster feedback loops, thus offering insights into quality status and release readiness. This shift is vital in a time where software complexity is a major cognitive load and businesses push for faster time-to-market to stay competitive.

The benefits of AI testing tools

Many of the benefits we see from AI are the same we see from automation in general, at least when it comes to everything ‘quality at speed’-related. Here are the top benefits:

• Enhanced development and QA productivity: AI-assisted test generation and maintenance can free up time and resources in QA teams, speeding up the overall software development process.
• Improved market responsiveness: Faster testing cycles mean quicker adaptation to market changes and customer needs.
• Greater test coverage and quality: AI can explore complex application paths more thoroughly than humans, reducing the chances of missed bugs.
• Reduced test flakiness: AI can help predict defects and refine tests dynamically, leading to more reliable tests.
• Compliance assurance: AI tools can help ensure that software meets regulatory standards by incorporating compliance checks into automated tests.

Reality check: the limitations of AI today

While AI testing tools offer impressive benefits, it's crucial to stay grounded about what they can truly accomplish today. Despite all the buzz about ‘autonomous testing,’ we are far from a point where testing can be done entirely without human input. For example, right now, AI-augmented tools can assist testers in things like test generation and maintenance, but human validation and oversight is still necessary to ensure the tests are accurate and relevant.

The best approach today is a partnership between humans and AI. Think of it as a team effort: humans set the direction, AI and automation do the heavy lifting in the middle, and then returns to humans for a final check. This collaboration allows AI and automation to manage repetitive, mundane tasks while freeing human testers to focus on more strategic, high-level activities that require creativity and critical thinking. 

Practical applications of AI testing tools

While AI testing tools are still in their early stages, organizations today are utilizing them in various ways. According to the 2024 State of Testing Report, here are the top 3 use cases for AI in testing:

• 25% are using AI for test case creation
• 23% are using AI for test case optimization
• 20% are using AI for test planning

Let’s dive a little deeper into the broader usage and capabilities of AI. Understanding the use cases will be important as you research AI testing tools and investigate capabilities. As with any technology, remember to think about the capabilities you actually need. Not all organizations will need all capabilities, and not all tools will offer them. Instead they might offer other benefits, such as a good technology fit, end-to-end testing capabilities, etc.

• Automated test generation: Leverage AI to derive test cases and scenarios from requirements or user stories, streamlining the initial stages of test planning.
• Synthetic data creation: Utilize AI to generate synthetic data that mimics production environments, addressing privacy concerns and enhancing test reliability.
• Visual testing: Employ AI for rapid identification of UI changes across multiple devices and platforms, crucial for user-centric and accessible applications.
• Optimization of test suites: Use AI to eliminate redundant tests, ensuring your testing process is both efficient and effective.
• Prioritization of test execution: Apply AI to intelligently select and order tests based on their importance and potential risk, optimizing test execution.
• Enhanced test and defect analysis: Integrate AI to analyze test stability and predict defects, allowing for proactive quality assurance.
• Effort estimation: Deploy AI to accurately predict the effort needed for testing, aiding in better resource management and scheduling.
• Strategic decision support: Harness AI to make informed decisions about the testing process, aligning it closely with business goals.
• Self-healing tests: Implement AI-driven updates to tests as application changes occur, minimizing maintenance and ensuring test relevancy.

Challenges and future directions

Despite the benefits, adopting AI in testing comes with challenges. These include the need for skills in AI and machine learning, interpreting AI-generated results, and ensuring security and data privacy.

In fact, according to the 2024 State of Testing Report, skills needed in AI/machine learning have gone up from 7% in 2023 to 21% in 2024. Conversely, in 2023, 50% saw programming skills as essential in testing, in 2024, that percentage was reduced to 31%. These shifts in skills requirements speak to the major shifts in the tools themselves, and how we’ve gone from fully code-based automation approaches to a much greater adoption of no-code, AI-powered tools.

And what about testing AI? 52% of IT leaders say they expect their organization will use GenAI to build software. With this comes the need to test the results AI generates, potentially using AI to do so…

As AI technology evolves, tools are becoming more capable and integrated, moving towards more autonomous testing. We’re certainly not there yet though - as mentioned, AI tools will continue to need assistance and validation from humans for quite a while.

That said, as we look to the future, the dynamic market and customer demand for increased automation and innovation suggest that AI-augmented testing tools will not only become more prevalent but essential in achieving high-speed, high-quality software delivery.

In conclusion, AI-augmented testing represents not just an upgrade in technology but a fundamental shift in how quality assurance integrates with and enhances the software development lifecycle. It's an exciting time for IT leaders to harness these tools to propel their teams towards more efficient and effective testing paradigms.

About the author

Maria Homann has 5 years of experience in creating helpful content for people in the software development and quality assurance space. She brings together insights from Leapwork’s in-house experts and conducts thorough research to provide comprehensive and informative articles. These AI articles are written in collaboration with Claus Topholt, a seasoned software development professional with over 30 years of experience, and a key developer of Leapwork's AI solutions.