Artificial intelligence has taken a front-row seat in almost every industry including software development. AI-driven technologies have transformed quality assurance processes in particular. Automating tests and predicting future system glitches is now easy. But that’s not all.
In this article, we will look at other incredible benefits and applications in QA.
Let’s get into it!
AI in Quality Assurance Statistics by Market Size and Growth
The following AI in quality assurance statistics reflect the tremendous growth this sector has seen in recent years:
1. AI in Quality Assurance is expected to reach $4 billion by 2026, up from 426 million in 2019.
This made something clear: businesses are catching on to how much smoother and faster things can go when AI steps in to help. It’s not just a trend; it’s a full-on sprint towards smarter testing.
2. According to Global Markets Insights, the AI QA sector in North America will reach a CAGR of over 25% from 2020 to 2026.
A CAGR of over 25% is the kind of growth you see in industries that are on fire with innovation. Tech companies in North America are really pushing the envelope.
3. The automation testing market is expected to reach $166.91 billion by 2033.
$166.91 billion for automation testing by 2033 is huge. It’s fast, it’s relentless, and it doesn’t miss a spot, which is exactly what you need when you’re rolling out app updates at the speed of light.
4. The market for AI-enabled testing tools is expected to reach $2 billion by 2033.
AI-enabled testing tools hitting $2 billion by 2033 show the increasing reliance on AI technologies to further empower the testing process.
AI in Quality Assurance Adoption and Usage Statistics
These AI in quality assurance (QA) statistics show how widespread the technology is among testers:
5. 78 percent of software testers use AI to enhance their productivity.
Nearly 8 out of 10 software testers are now using AI to get more done. This suggests that AI tools are becoming integral in streamlining workflows and reducing manual testing burdens.
6. 44 percent of companies state that they have integrated AI into their QA processes.
Almost half of software development companies have brought AI into their quality check routine. They’re getting the robots to do the heavy lifting, which probably helps in automating repetitive tasks and ensuring software quality.
7. 19 percent of companies plan to adopt AI in QA within the next two years.
About 1 in 5 companies haven’t jumped on the AI bandwagon yet but are gearing up to do so pretty soon. They’ve probably recognised the potential of AI in QA and are making plans to step up their game in the next couple of years.
8. 64 percent of companies will adopt AI into QA frameworks to improve customer processes.
More than half of the surveyed companies are planning to let AI take the wheel in QA. They’re not just looking to catch glitches; they want to make sure users have a smoother experience with their product.
9. 90 percent of software testing companies use automation
9 out of 10 testing companies are interested in automation now because it’s faster and more accurate.
10. Developers use AI for functional and regression testing 73 percent of the time, compared to unit testing which is done 45 percent of the time
Developers are leaning on AI mostly to execute functional and regression testing. Unit testing is also done with AI but less frequently. This suggests that testers prefer using AI in broader testing scopes.
11. 74 percent of QA teams conduct automated tests without implementing prioritization systems.
This number tells us that most QA teams are running automated tests on the regular, but they’re not sorting these tests by importance or urgency. Without a system to prioritize, there may be inefficiencies in addressing the most critical issues first.
12. 72 percent of organizations engage testers in sprint planning sessions.
Almost three-quarters of companies involve testers in the early stages of agile development cycles. This means testers are getting to weigh in on the game plan from the get-go, which is super smart.
13. But 61.60 percent of organizations involve testers in every sprint.
While many teams include testers in planning, a slightly smaller percentage involves them in every single sprint. Continuous tester involvement can lead to more consistent and immediate feedback throughout the development process.
14. The most popular use case of AI in QA is automating the creation of test data which is about 51 percent of the QA process
The primary application of AI in QA for over half the organizations is in automating the generation of test data. It’s a huge time-saver.
15. The second most popular AI use case is formulating test cases which is about 46 percent of the QA process
Close behind, nearly half are using AI to help put together the actual test cases.
16. 89 percent of companies are using CI/CD tools to automate test deployment and execution
Almost 9 out of every 10 companies have embraced CI/CD tools to make sure their software tests run automatically. These tools reduce the scope for human error and accelerate deployment cycles.
17. Only 45 percent of organizations manually trigger their automated tests.
Less than half of the organizations initiate their automated tests by hand. This number suggests that there’s a significant movement towards fully automated pipelines where tests are triggered by system changes rather than manual intervention.
AI in QA Impact and Success Rate Statistics
The following AI statistics demonstrate the power of AI in testing. These stats are good reasons why every QA team should consider using AI:
18. Vijay Shinde, the founder of Software Testing Help, believes AI can cover about 70 percent of repetitive testing tasks.
This opinion by a thought leader in the field indicates a strong confidence in AI’s ability to take over the routine aspects of testing. As time goes on, more and more testing tasks will run automatically.
19. Test automation has replaced 50 percent of manual testing efforts.
This number is a big deal. If half of all the work testers used to do by hand is now being handled by machines, we can expect test processes to be more efficient.
20. About 24 percent of companies have successfully automated more than half of their testing process.
About 1 in 4 companies have reached the point where over half of their testing is done without human hands. They’re on the automation train, but there’s still plenty of track left for others to hop on.
21. 42 percent of organizations state that test automation is a key part of quality assurance.
Just under half of all organizations recognise that having automated testing is crucial for maintaining the quality of their products.
22. AI automation could optimize over 70 percent of IT expenses, much of which centers around QA.
AI’s potential to cut IT costs is huge, especially in quality assurance where it can take over repetitive tasks and make the whole process more efficient.
23. Forrester predicts 15 percent productivity gains for software testers who use generative AI.
According to Forrester, software testers who use generative AI technologies are going to see their productivity jump.
AI in QA Investment and Funding Statistics
Check out these AI in quality assurance statistics to understand how invested tech leaders are in this technology:
24. AI-related investments in software development were the second largest in 2022, with about £1.7 billion in funding.
Investors are putting their money on AI in software development. Their huge investment in this sector reflects AI’s growing importance and the industry’s confidence in AI’s ability to advance development practices.
25. Most companies using AI to automate testing allocate 10 to 49 percent of the QA budget to test automation.
Companies jumping into AI for testing aren’t just dipping their toes—they’re spending a decent part of their QA budget, anywhere from a tenth to almost half, on automating testing strategies.
AI in Quality Assurance User Experience Statistics
The following AI statistics are about what to expect when to incorporate AI into your QA workflows:
26. 68 percent of testing experts stated that AI is the most significant innovation in software testing for the future.
A strong majority of professionals in the testing field believe that AI is the game-changer that will shape the future of software testing.
27. Data quality is the most significant barrier to adopting AI in quality assurance.
The biggest challenge that companies face when it comes to embracing AI for QA is ensuring that they have high-quality data because good data is crucial for AI to function effectively.
28. 55 percent of companies adopt automated testing strategies primarily to improve product quality.
More than half the companies leaning into automated testing are doing it for one big reason: to make their products even better.
29. At the same time, 30 percent are mainly interested in accelerating quality product launch
For some companies, their reason for focusing on automated testing is to speed up their time-to-market. They’re looking to get quality products out the door faster, with automation providing the necessary efficiency to do so.
30. 26 percent of organizations state that their biggest issue with test automation is finding the right tools.
More than a quarter of organizations are finding it challenging to select the appropriate tools for test automation. With hundreds of options available, choosing the best fit for their specific needs can be overwhelming.
Conclusion
These AI in quality assurance statistics should serve as a guiding light as you implement AI-driven technologies into your workflow. You’ve already seen how this technology is disrupting the QA field.
If you’re considering hopping on the bandwagon, try out QA.Tech. We provide reliable autonomous testing for B2B and SaaS web applications.
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FAQs
1. Why is AI in quality assurance statistics important?
Understanding AI statistics in QA can help your business stay ahead of the curve by adopting the latest technologies that enhance efficiency, reduce errors, and speed up product delivery. These statistics provide insights into industry trends, helping you make informed decisions about where to invest in your QA processes.
2. What is the future of QA automation?
With the continuous advancements in AI and machine learning, QA automation is expected to become more intelligent, with capabilities to not only execute predefined tests but also become fully autonomous.
3. Can quality assurance be automated?
Yes, many aspects of quality assurance can be automated. Many testing processes and bud detection techniques today run automatically. However, not all QA tasks can be automated—some will still require human perspective and judgment, especially when it comes to usability and exploratory testing.
4. What is the role of AI in audit?
AI enhances the efficiency and depth of audit processes in so many ways. It can analyze vast datasets much more quickly than humans can, allowing auditors to identify risks that might not be seen otherwise. It can also assist in continuous auditing practices, so testers can monitor quality in real time.
