How, When, and Why Top QA Engineers Use AI in Testing
AI can help QA engineers with tasks like developing expert systems simulating human behavior or making data-driven decisions about test cases. Learn more here.
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With the increasing popularity of AI, there are more and more jokes about replacing programmers with AI: "To replace programmers with robots, clients must accurately describe what they want. So we are safe."
As artificial intelligence spreads worldwide for both business and personal use, it’s helping developers work smarter and more efficiently. AI can assist QA engineers with various tasks, from developing expert systems that simulate human behavior to making data-driven decisions about test cases. How can AI help QA engineers? Let’s try to answer this question.
Why Should Software Testers Use Artificial Intelligence?
Automating parts of the software engineering process is a common practice in many industries. This includes QA as well. AI enables testers to do their jobs faster and more efficiently, allowing them to focus on their core responsibilities. It can improve their productivity and quality by enhancing the understanding of how people interact with software and systems through monitoring users’ actions, analyzing past events, and predicting future ones.
AI in the QA process can be realized in the following ways:
- It can learn from past experiences and apply what it knows to similar situations, enabling it to make more decisions with less human input.
- AI can help find bugs before they happen again and make regression testing a comprehensive process.
- It can analyze data faster than humans ever could, resulting in more thorough testing of products or services, meaning that it can be used to support almost every role within a company.
- It can make your work easier and more efficient by automating repetitive tasks like bug reporting or test case management.
One of the main ways that QA engineers use AI is by using machine learning models. These can analyze data from past tests and other similar programs to identify patterns within those programs, which could indicate potential problems down the road when users start using them in real-life situations outside of controlled environments. One example is finding and prioritizing essential tests with a model that identifies tests that historically give you the highest number of severe issues, then auto-creates test scenarios that group into impactful tests. The second way is by using AI to automate their tests. It can help ensure that they find any bugs quickly before they get out into the world, where they could cause problems for customers or even worse damage.
When To Use AI in Testing
Let's first find out the difference between automation and AI in QA testing. AI brings together automation, machine learning, and statistical analysis to improve testing efficiency and accuracy. It also helps developers gain a broader perspective of how their applications behave as well as an ability to detect critical issues faster due to extensive test coverage.
Our task is not only limited to highlighting needed areas for improvements — it also brings insight into your organization's agility by analyzing factors such as changes in requirements over time, development costs, and team growth.
AI can help with warnings and false positives, speeding up SLDT ten times faster. For example, it helps with false positives. Automated testing is excellent, but it still can bring an enormous amount of false positives about events that are not actual bugs or multiple warnings that usually don't happen when manually testing. Such a thing kills development and QA teams, who have to repeatedly review the existing code to eliminate possible problems. Imagine their frustration when they find out that it was a false positive!
What can AI do for them? The solution can be creating a classifier based on AI.
"This classifier is based on results from previous classifications of static analysis findings in the context of both historical suppression of irrelevant warnings and prior prioritization of meaningful findings to fix inside the codebase." - Igor Kirilenko, Parasoft's VP of Development
AI is another area where it is used along with UI for testing purposes. This can be seen in many kinds of UI test automation using AI and ML. In this era of AI and ML, developers are looking for solutions that create high-conversion websites faster and are less expensive than ever.
An excellent example of using AI to achieve these goals is when developers try to integrate it with the Selenium framework to automate the process of creating quality websites. However, they still need some help executing these tests so efficiently. For example, they need to learn how to solve some problems associated with the tests, like time taken to run a test, stability execution issues, etc.
Key Benefits of Using AI in Testing for Top QA Engineers
Get ready to see a huge difference in your QA process with the help of AI. Here are the top 3 ways AI can make your QA process better.
The presence of AI can level any type of disagreement within a team. AI also prevents testers from burning out during monotonous and uninteresting testing thanks to the fact that AI helps speed up results and eliminate team contradictions.
Better Defect Tracking
Another advantage of AI is that it allows you to discover vulnerabilities in your product that might be similar to the ones already detected. This, in turn, will let you eliminate it immediately and fix all possible problems. Artificial intelligence also extends to fixing some bugs without the explicit presence of developers in this process.
Wider Test Coverage
AI can kick off testing with a broader scope than automated or manual testing, even when the application is not explicitly provided with the necessary information. Because AI can view internal states, it can decide where additional testing needs to be done.
Instead of a Conclusion
We asked AI about the benefits of incorporating it into your work process and to tell the truth, we were pretty surprised. This is what AI said:
“You’ll have more time to spend with friends and family. No longer will your work days be filled with endless document reviews and bug reports — now they’ll be filled with a happy hour with friends, spending quality time with loved ones, and whatever else makes you happy!
You can sleep at night knowing that your job is secure. As long as humans on this planet want things done right (and done right now), there will always be a place for QA engineers.”
AI has already become an essential part of our life. Do you really think that AI doesn't have a soul?
Published at DZone with permission of Martin Koch. See the original article here.
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