Why AI Won't Replace Your Testing Job Just Yet

Mainstream audiences and critics have long been coy over the topic of artificial intelligence, and movies like the Terminator series have made the fear of a Skynet clone a damning fear for most. But a more immediate cause for concern for people is that before AI overlords rule over our planet, they’ll replace us at our jobs, making human efforts redundant.

If you’re a tester, part of your job has already been taken over by AI. But is that a genuine reason to fear AI, especially with other industries catching on to its implementation? Perhaps not.

Currently, we are seeing a trend where automation has transformed existing job roles at the cost of human employment. But automation has typically also caused an influx in new jobs that require new skill sets. Things change slightly when AI is brought into the mix.

While automation is meant to replace manual tasks but it needs to be designed and developed by humans. AI is different in that it is a computer program that has taught itself to carry out a task, which makes it more adept at the job than all but the most skilled people in the world.

What complicates the matter even further is that most AI models escape our understanding, and with our predisposition of fearing things we don’t fully understand, AI is yet another addition to this list.

Not too long ago, things and mechanisms like these were branded as the work of the devil or simply magic, so it’s to no one’s surprise that artificial intelligence is getting a bad rap too. Today, we’ll be looking at some of the reasons why AI isn’t going to end your career as a QA professional or tester but may help you enhance the selection of tools that you currently have at your disposal.

The most important thing to understand right now is that artificial intelligence can perhaps never replace manual testing, just like test automation has failed to do so. This is because manual testing is used when you are trying to recreate the steps to detect a bug or error.

Another reason is that test automation just doesn’t work well with progression or exploratory testing. And because AI requires learning through repetition, it is next to useless for one-off testing. For these two significant reasons alone, manual testing will always be required. 

Senior managers can often overlook an important aspect of software testing. Namely, the skill needed to define and make robust test plans. It’s relatively easy to show a developer the way to create test scripts. But there’s an enormous difference between creating a test script and creating a test suit.

Even given a test suit, you would like a particular level of skill and knowledge to make a strong test script. Fundamentally, you would like to know how the end-user actually interacts with the software. 

So what’s the bottom line? Manual tester’s jobs are safe, and test automation specialists are consigned to helping design test cases and test plans, right? Not really.

AI is excellent at building tests, even quite complex ones. But if you add in some test automation expertise, you'll create much more powerful tests. These effectively allow you to feature programmable functionality in your AI-powered scripts.

So now that we’re all on the same page and understand that AI won’t take away anyone’s job, how can it benefit testing? That’s arguably the best part about artificial intelligence in that it can take data and show new insights that may have gone unnoticed before. This functionality is incredibly useful in testing. Here are two of the most important ways AI can benefit software testing:

This is essential to ensure that you’re testing the proper things. Most test case management tools allow you to gather data on how your users really interact together with your application. This enables you to spot gaps in your testing and makes it very easy to make tests that fill those gaps. The testing tool’s visual testing approach also allows you to spot usability issues in your code, like elements that always take an extended time to load.

We would all wish to believe that we write perfect test scripts that are well described and straightforward to take care of. But in fact, the reality may be a bit different. For a start, it’s hard to accurately explain what your test was designed to try to do. It becomes even more difficult after the script has been through a couple of dozen rounds of updates and revisions. And don’t forget the very fact that your software will have evolved within the meantime. AI-based testing mitigates these problems by maintaining legacy test scripts.

Tests are often debugged step-by-step, and you see live screenshots, so you recognize exactly what's happening. You'll also turn back the clock and examine closely to see how the test has evolved over the span of time. That’s a marked contrast to plowing through many lines of script in test case management tools trying to figure out what it had been testing and why it mattered. 

AI isn’t something you should be worried about, and though that’s mostly the case, complacency is the bigger enemy. The boom in the implementation of AI in testing will see the emergence of new skills that are highly demanded. This would be of an AI test automation engineer, and they’ll understand how AI works and how to incorporate it based on its limitations and benefits.

And perhaps more than that, they’ll be open to the idea of utilizing AI to increase their productivity. In that time, you wouldn’t want to be the one that’s left behind when the rest of the industry has moved on.