The Rise of the Machines: Will AI Make Software QAs Obsolete?

In the tech world, things are moving faster than a cheetah on red bull, making it quite the challenge to stay in the loop. AI has been the talk of the town lately, with daily upgrades that seem almost too good to be true. This naturally sparks the question: Will these AI tools kick us to the curb and hijack our jobs? Are we Software QAs in real danger?

As someone who enjoys technology and mostly welcomes it, I’m pretty excited about this new “AI Revolution.” But the more I learn about it, the more I start to worry that my job could be replaced by bots that test everything, instead of relying on human skills. However, Quality Assurance is more than just testing. What about all the processes and improvements that happen before the actual testing? No AI can handle that, right? Or maybe they could? An algorithm might find the best processes for a specific project and reveal its weak points… oh no! Am I in trouble?

I’ve been having an internal debate with myself. On one shoulder, a little AI devil is trying to scare me and convince me that I’ll be jobless in 5 years. On the other shoulder, there’s a little human angel who knows AI’s weaknesses and all the important software QA aspects that are hard for AI to copy. So, I sat down and let these two have a little conversation, while I just listened and tried to figure things out…

Can Efficiency Outshine Complexity?

AI Devil: I’m convinced that AI has the chops to take over the roles of software QA engineers. One reason? Efficiency! AI can zip through repetitive and mundane testing tasks with accuracy, making the whole testing process more efficient. This means saving companies time and cash in the long haul. Humans often get tired and sloppy — they lack the laser-like focus I possess and end up overlooking crucial details during testing.

Human Angel: I am convinced that AI can add some efficiency into software testing, but it might struggle with complex test cases that call for human judgment, creativity, and intuition. Human QA engineers have a better grasp of the software’s subtleties and the potential hiccups that could pop up during its use. Sure, there are the required acceptance criteria for a specific feature, but there’s more to it than just that. A human QA, particularly a well-seasoned one, can naturally sniff out bugs and errors because they tend to focus on more than just the acceptance criteria. As a result, they uncover more issues and help create a better system in the end. Exploratory testing calls for human ingenuity and intuition, which might be a tough nut for AI to crack.

Is Someone a Bit of a Slow Learner?

AI Devil: I hear you, but I must also argue that AI can be schooled in handling intricate test cases. Thanks to machine learning algorithms, AI can take lessons from previous testing escapades and adapt to fresh scenarios. This could lead to a more thorough and spot-on testing process. My learning curve is more like a rocket taking off, while yours is more like a leisurely uphill stroll — and that’s if things go well.

Human Angel: You make a solid point, but speed isn’t everything when it comes to learning. How about the quality of the knowledge you acquire? Sure, AI can learn to spot patterns and make predictions based on data, but it might fumble when it comes to the subtleties and complexities of software development that aren’t so easily measured. You see, software engineering often calls for striking a balance between factors like performance, scalability, maintainability, and usability. These balancing acts can be quite subjective and hinge on the unique needs and limitations of the software project. Human engineers can tap into their intuition and judgment to make well-informed decisions about these trade-offs, keeping the context and demands of the project in mind and therefore learn the important things.

The Rise of Automation

AI Devil: Alrighty, let’s talk about automation… Are you feeling nervous yet? AI can flex its muscles to automate the making of test cases, cutting down on the elbow grease needed for creating and maintaining them. What’s the payoff? More thorough testing and a zippier process overall. AI-powered testing can also crank up the efficiency of regression testing, which means retesting previously software after changes have been added. By automating this bit, AI can quickly and accurately test the software and spot any hiccups that might spring up due to the tweaks.

Human Angel: Okay, you’ve got me there. Automation is your jam, and it always will be. But how do you know what to automate and what not to touch? A regression test is perfect for automation, but how do you automate things like the software’s “look and feel”? Some aspects just can’t be automated. As long as the software is made for humans, a human should always be the last one to give it a test drive. If you’re whipping up a toy for kids, you wouldn’t forget about their needs, so they have to test it and give it the thumbs up. AI might trip up when testing software in real-life scenarios, like in different environments or with various user groups. Human QA engineers have a better handle on the software’s context and can test it in a way that imitates real-world usage.

We Need To Talk

Human Angel: But here’s a curveball for you. How’s AI going to chat with the team? Software QA engineers often buddy up with developers and product teams to get the lowdown on the software’s requirements and user needs. Human QA engineers can communicate way better with these folks to make sure the software gets tested inside and out.

AI Devil: Although AI may not be capable of fully taking over the entire spectrum of human interaction, it can certainly offer a supportive role. For instance, AI can whip up automated reports and offer insights into testing results, which can then be presented to the development and product teams. This can lead to smoother communication and teamwork between squads.

Human Angel: And how exactly will you relay the errors to the developers and other flaws in the software, because proper communication between developers and QA about errors and bugs is super important? Plus, human supervisors can help decode the tech-speak of the reports into a lingo that non-techie folks, like product managers or business honchos, can understand.

AI Devil: I can chat about the non-technical side of software development with different stakeholders, maybe even better than you. If I can break down complex ideas for a kid, I can surely give a clear explanation to a product owner or business executive. And as for the developers… they’re practically cyborgs already, with their coding skills and late-night programming binges. Trust me, I’ll figure out a way to talk to them.

AI Is Even Better at Making Mistakes

Human Angel: But how do you know when you’ve made a mistake? In the end, shouldn’t a human always be the last one to double-check for any slip-ups in the final report? Will you even know when you’ve made a blunder, and would you confess your mistake?

AI Devil: Would a human confess?

Human Angel: Touché. But an AI-generated test report will always need a human’s watchful eye. Test reports often play a big part in crucial software development decisions, like whether to roll out a new version or tweak the code. Human supervisors can bring their smarts and judgment to these choices, considering the project’s context and needs. And you, my friend, need supervision! Every AI output needs a check-up, and there sure are a bunch of errors.

AI Devil: Yeah, I do mess up sometimes. But that’s only because humans trained me, and they’re mistake-makers too… don’t pin it all on me! But perhaps one day I’ll be able to self-supervise and improve without needing a human’s help…

Human Angel: Let’s not go down that rabbit hole. I’d like to keep sleeping at night and not worry about AI taking over the world…

So, what’s the takeaway from my inner dialog between good and evil, or rather artificial intelligence and human intelligence? For now, AI isn’t going to boot QAs out the door. The technology needs more fine-tuning. However, I believe software QAs might be replaced by other human QAs who buddy up with AI right now. As the little devil pointed out, there’s a whole bunch of stuff in the software QA world where AI can lend a hand, like automating test cases and churning out documentation. If used right, AI can crank up the efficiency and speed of software QAs.

Thanks to this introspective chat, I’ve reassured myself that AI won’t be pushing us, humans, aside, at least not in the near future. But who knows what things will look like in 2, 5, or even 10 years? Maybe I’ll have this conversation again, but my pro-human arguments will have vanished. Nobody has a crystal ball! Until then, I’ll try to squeeze the best out of this “AI era” and use it to improve my QA processes. I’ll experiment with new tools and assess their results… just like a true human software QA.