Testunity

AI-Generated Tests Are The Future of QA

 

With the requirement for frequent builds—often many times in a day—AI-led testing is the modern strategy as it enables quality engineers to create scripts and autonomously run tests together to detect bugs and provide data to get to the root cause. It’s time to get rid of slow and ancient methods and experience this new form of QE improvement!

AI-driven testing could mean several things to different QA engineers. Some may see it as using AI for recognizing objects or helping create script-less testing; others might think of it as autonomous generation of scripts while some others would consider it in terms of leveraging system data to generate scripts that mimic real user activity.

In the light of a now growing AI test technology, these are all possible functions that AI can drive in quality engineering.

While the majority of testers and engineers have still to experience this form of QE improvement, let’s examine how this kind of QA is done. You don’t need to risk falling behind.

The Waterfall method needs months and even years. So why are engineers sticking with it?

Old habits die hard. The approach we use to test software today stems from the work at Mercury Interactive in the mid-1990s where they established a method of coding to automate user actions. This code needed maintenance at each new build. But this was fine as long as a new build was required only a few times a year. However, over a span of time, the code to test the software would exceed the software itself in the effort.

Turning the clock ahead by 25 years to today, we find that the majority of test automation remains to use this waterfall approach of testing. We spend months or even years writing tests that were mandated by a business analyst. Then throw large resources at maintaining those tests.

While we may have shifted from QTP to UFT to Selenium (, the process flow is the same. However, rather than a new build a few times a year, we have new builds several times a day, or perhaps a few times an hour.

So, why are we still using a method designed to take months in a workflow? Because the open-source contributions didn’t change the process. So that’s what the industry gave us. Basically, new language but the same process that was used in 1995.

 

Re-imagining the testing world

Currently, two technologies are needed in one platform of four million lines of code So, we are speaking 100X the size of anything in the open-source world.

The plan was that what the Business Analysts want testing is not completely the best way to discover bugs, or even mimic real user activities. It’s their estimate as to how people may be using the software, website, or mobile app. It’s not a bad guess, but it is a guess. If this is true, we should be able to dramatically improve our ability to find bugs in and around those use cases and behind them. In addition, we require to lessen script writing, make it much faster and far more flexible to accessor changes.

And we would require to generate many tests fully autonomously. Why? Because applications today are 10x the size they were only ten years ago. But your team doesn’t have 10X the amount of test automation engineers. And you have perhaps 10X less time to do the job than ten years ago. It will need each engineer in your team has to be 100X more productive than they were 10 years ago. But as the test automation tools have not changed, and humans didn’t magically get 20 arms and more brainpower, the automation engineers will never be able to catch up with the requirements of the organization and recognize bugs before your users do.

Let’s face it, there is no way to catch up. You understand this already. We take shortcuts, drop many tests, disregard the results of others. Do the best we can with the limited resources at hand. And the next build will append more features and pages and states. And that suggests there is more work to do each day and you’ll remain to fall further behind. Forever.

Unless you change something. And that something is AI.

 

AI-testing in two steps

We leveraged AI and observed over 90% reduction in human effort to find the same bugs. So how does this work?

It’s really a two-stage method.

First, leveraging little bits of AI in Test Designer, TestUnity’s codeless test creation system, we make it possible to write scripts faster, recognize more resilient accessors, and substantially decrease maintenance of scripts.

The systems can select the most stable accessors, quickly rerun that script several times to find even better accessors, and build a repository of accessors automatically. Fall back to those accessors when tests work and hit an accessor change, and eventually self-heal scripts which must be refreshed with new accessors. These four built-in technologies give you the most stable scripts each time with the most robust accessor methodologies and self-healing. Nothing else comes close.

The ultimate two points above deal with an autonomous generation of tests. In order to beat the queue and break it, you have to get a heavy lift for detecting bugs. And as we have learned, go far behind the use cases that a business analyst listed. If job one is to find bugs and then prioritize them, leveraging AI to create tests autonomously is a godsend.

In general, an AI engine, which already has been equipped with millions of actions, attempts to create real user flows and takes all possible action, exhibits every page, fills out every form, gets to every state, and confirms the most critical outcomes. All without writing or reading a single script. Fully machine-driven. This is called blueprinting an application. And you do this at each new build. Often this will produce 1000 or more scripts in a matter of minutes, operate them themselves, and hand you the results including bugs, a load of data to help discover the root cause, and the scripts to repeat the bug. A further turn of the crank can filter these scripts into specific replicas of what production users are doing and apply them to the new build.

 

Any modern method to continuous testing needs to leverage AI in both helping QA engineers build scripts as well as autonomously perform tests so that both parts work together to discover bugs and provide data to get to the root cause.  

At TestUnity we have an expert team of QA Engineers. This enables us to give our clients the support they require to ensure that their software hits the market in the right circumstances.  When it comes to QA, nothing is better than having the correct people in charge. That’s why we make sure that everyone in our team is qualified and accredited on some of the industry’s best practices. 

Contact us for a free consultation and see for yourself why TestUnity’s QA approach is the best choice for your software.

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