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What QA Managers need to know about testing tools with AI

Artificial intelligence and machine learning are becoming valuable tools to software developers, QA managers and testers. While AI is not a magical solution to all the problems in QA, it can help you, and your team be more efficient, streamlined and focused on business goals and outcomes rather than wasting time with low-impact tasks.

The role of AI in software testing automation

The role of AI tools for test automation is doing things at a scale that humans can’t achieve: Automation solutions make QA

  • faster
  • more precise
  • innovative

We continuously look at ways to innovate on manual testing, using AI solutions as part of test automation. Using this technology, we can create new tests without human input. And that was also a crucial part of our test automation tool functionality within aqua. Our research found that it’s most helpful when there is a database of known good tests for the AI to draw inspiration from, and the results were quite promising. We can’t wait to make this available in a public beta!

Benefits of using AI tools for QA manager

There are a lot of benefits of implementing AI automation. Let's explore them further:

AI tools bring tangible efficiency gains. By offloading manual tasks to the AI engine, test managers can focus on higher level tasks such as monitoring and optimization.

It takes 30 minutes to do a full run of all automated tests using our solution. And the only way to reduce this time is to remove some tests. AI can help you build a model based on historical data and performs more intelligent analysis that allows us to focus our testing efforts on more critical areas and test components that are more likely prone to bugs.

Grouping tests is another way of optimising your test suite. AI allows you to optimise your test suites using various metrics, like coverage and error types. So even if you don’t have inconclusive or rarely failing tests, there are cases where you can still avoid running all of them.

You can also bundle user interface tests the same way. You don’t need to wait for visual tests to finish before releasing a new feature. AI can group them, and you can run the tests later if required.

AI-powered tools can also** bring smart work allocation** and help manage your team’s workload and priority. AI solutions can gobble up a vast amount of data to predict how an application will behave. It can then flag potential bugs or UI issues for devs to fix. If a project has multiple features and moving parts, these tools can be used to plan out the most critical tasks for each dev.

Having your software** assess the severity of discovered bugs even without scheduling** is benefitial. It allows you to get an overall picture of quality, which can be difficult and time-consuming with other forms of testing. It also helps you better prioritize what to fix now and what to improve later.

Time management is another essential skill that QA specialists learn to master. AI might be able to help us manage our time better, as experts say that no one-size-fits-all technique works for every tester. The best part is that once you know this potential challenge well enough, you can address it.

Poker planning is one of the most exciting applications we see in AI. AI analysis can be the tiebreaker when you have a similar decision and can't convince your colleagues or clients. It will look at everyone's vote across a few planning sessions, showing who has been more or less accurate than the actual effort.
Pitfalls of using AI for software testing

You should be aware of potential issues when implementing AI in your environment.

Given that** AI solutions are hard to validate**, AI-focused tools are too fresh from having a significant number of reviews. Traditional players will mostly have thoughts that were written before the vendor introduced AI functionality. They also happen to have a less accessible and sometimes gimped trial offering compared to up-and-coming companies.

AI is not a silver bullet. AI solutions can help testers to prioritise testing and automate tests efficiently. However, one should not expect 100% accuracy from AI-generated tests and/or prioritisation. You should expect automation to be a complementary tool that augments human expertise, not replaces it.

One of AI solutions' most significant challenges is committing to quality and stability on both sides. Ultimately, you will either need to help the vendor tune their AI with your input or learn to work around the quirks of a less interactive implementation. It can be a constraint because it takes time and money away from other quality assurance solutions that could otherwise improve your product more quickly. If you choose a promising AI startup over an established player, make sure you are aware of their financial stability.

The goal of AI-QA is to make tests more efficient and accurate. The technology helps test teams to reduce manual work, thus enabling teams to focus on the more difficult manual testing challenges like exploratory testing and finding critical bugs.

If you want to follow my testing learning journey, follow the “Software Testing Talks” groups I created on Reddit and Linkedin. I share the most interesting QA discussions I find on the web and insights I get during testing work and studies there.

I am also happy to hear your feedback, suggestions, or ideas about what you would like me to write more about. Don’t hesitate to text me if you want to say hi or discuss something. what you would like me to write more about. Don’t hesitate to text me if you want to say hi or discuss something.

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