The Quantum Leap that AI Can Provide to Testing

The buzz around ChatGPT and GPT4, the latest release of the large language model from Open AI, has not abated since it burst onto the tech scene several months ago. Many dev and testing teams are experimenting with leveraging the model to automatically write test scripts. This certainly has its advantages, vastly reducing the time to create tests.

And other applications and integrations of test automation platforms and GPT are almost certainly in the works. But is this truly the silver bullet for AI in testing?

The Ideal AI Use Case Profile

No matter how great a hammer is, not everything is a nail.  Applying AI to a problem has to be fit for purpose for it to be productive. It has to fit an Ideal Use Case Profile or IUCP.

What defines an ideal use case profile for AI in software testing AI?

First, the machine learning model has to have access to relevant big data to train the AI adequately. (Check for GPT.)

Second, the task for the AI to tackle has to be sufficiently routine, requiring more persistence than creativity. Besides the fact that such mundane work is far from the best allocation of human capital, it often falls prey to the very human tendency of boredom.  This, in turn, leads to spotty execution which can result in detrimental business outcomes, since routine tasks are still important,  mission-critical work. (Also check for GPT.)

Third, the cost component has to be sufficiently high to warrant taking the task away from humans and giving it to AI.  Labor is expensive, even if you offshore your QA to regions with cheaper resources. (Again, check for GPT.)

Fourth, the software testing process has to benefit from being close to the managers responsible for its outcomes.  Sending your QA function out of house inhibits collaboration, agility, and any guarantees of high-quality execution.  That is why business processes comprised of routine work tasks are better automated than outsourced. (Check again.)

Fifth, the AI needs to work at a pace or in a way that a human cannot. GPT can certainly work at the speed that a human automation engineer can’t when it comes to writing a specific automated test script, but can a human design the needed tests as efficiently as AI itself?

AI in Testing: The Unique Opportunity

In testing, using AI to write the test cases that the automation engineer can’t envision (and doesn’t have to prompt the AI to write), represents a unique opportunity, with value that goes beyond the speed advantage of using AI to write specified tests.

In particular, AI has a great source of big data with which to train the AI for a particular app: the app’s production logs which software publishers keep in Apache, W3C logs, or log managers (e.g. Splunk or SumoLogic). They are a record of users traversing existing functionality.  While these logs have previously proven a boon for SysAdmins, they can now be further exploited for QA as a big data source for AI-driven regression testing.

In short, regression testing fits our IUCP perfectly, making it well-suited for an AI solution.

Application Coverage and AI

But that is not the end of the story.  It is not even the best part of the story.

AI can also explore all the possible user flows in your application, meaning it can write tests for every possible user action—not just the flows you specify, nor the flows pursued recently by your users. It can cover every user flow, including entirely new paths enabled by new code in your latest release.

Appvance’s AI model can predict outcomes to create a truly hands off approach to testing, ideal for use as a smoke test of your latest release.  And by integrating with modern CI/CD systems, AIQ brings that functionality to a Continuous Testing regimen, taking it to new levels.

When new code is committed to the mainline and automatically compiled, the new build can be autonomously tested by the AIQ AI.  It happens quickly, efficiently and automatically, providing not just test results, but also a uniquely complete view of the status of your application through our proprietary Coverage Map.  It is a breakthrough in efficiency, agility, and quality improvement.  And it is only possible by employing the autonomous testing AI found in AIQ.

Want to Learn More?

If your team is ready to retire legacy automation tools and adopt a future-forward autonomous AI tool in your regression testing or for smoke testing each release, now is a great time to get a live demo of AIQ. We would be happy to show you how AIQ and its advanced machine learning model can increase the productivity of your QA function in ways traditional tools cannot begin to address. Request a meeting today!

And to learn more about AI-driven autonomous, continuous testing, download our eBook here.

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