Article

AI in QA: How QA Testers Use Artificial Intelligence in 2024

Jess Feldman

Written By Jess Feldman

Liz Eggleston

Edited By Liz Eggleston

Last updated on May 30, 2024

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Worried that AI is going to replace the need for QA Testers or Quality Assurance Engineers? While the potential for AI is expansive, Stephen Hudson, the Curriculum Lead at TripleTen, shares why AI won’t reduce the need for QA Engineers in our lifetime! Stephen explains how AI may be used on-the-job in QA, and how TripleTen keeps its finger on the pulse of technological breakthroughs to make sure current students and alumni are job-ready. 

Meet The Expert: Stephen Hudson, Curriculum Lead at TripleTen

  • Skilled in software engineering and QA, Stephen is currently the Head of Content for the new QA program at TripleTen, working cross-functionally with the TripleTen team to shape the content to the students' needs.
  • Stephen utilizes specific feedback from students and from community managers, tutors, and code reviewers to help iterate the current curriculum. 

Stephen, many people are concerned about the rise of artificial intelligence, particularly that AI could replace tech careers like QA Testing or QA Engineering. Is this a legitimate concern?

As AI gets more media coverage, it's natural to have concerns about jobs. That concern is rooted in truth – AI may help people complete jobs more efficiently and save time. Where it might take 10 QA engineers to do a specific job now, in the future it might only take two or three engineers to do the same job. 

AI isn’t going to be replacing some people’s jobs; people who are skilled in AI will be. Similarly to how it’s become a requirement for most people to know how to use the internet, I think it will become a requirement for people to be able to use AI. 

That said, I personally don't think the number of QA engineers or testers will actually be reduced. I think the volume of work will increase and so the amount of QA engineers will stay steady or increase. It’s compounding. There will be more technology, more projects, and more needs to test these projects. 

Keep in mind that about 90 years ago there was a prediction that technology would allow everyone to work 15 hours per week, which would've been fantastic if it had come true! But what tends to happen is that as productivity increases the amount of work that needs doing also increases. A company could choose to do the same amount of work in half the time but often opts for doubling the amount of work with the same resources. 

Let’s back up for a moment — what exactly is Quality Assurance? How do QA Engineers contribute to a technical team?

Quality Assurance (QA) engineering is focused on ensuring the quality of a product. QA careers have existed long before computers, but QA engineers specifically ensure the quality of software products. As important as QA is, it's one of the simplest jobs to learn! QA engineers might assess a product for performance, stability, usability, or security, varying by the individual product and project they're working on. QA Engineers don't need to know math or programming languages to do their job. Any coding they do on the job involves simple commands and logical sequences. 

QA engineers work on the overall quality of a piece of software whereas a software tester executes tests (that they may have written) and reports on the bugs they find. A big enough organization might have enough resources to split these roles in this way, but in reality, there's so much crossover and job postings often describe the roles similarly. Testing is still a subset of the QA engineer's overall responsibilities.

What does a QA Engineer do that AI could not replace?

If we’re thinking of the next 5-10 years, humans will continue doing the more strategic tasks, whereas AI will do more specialized tasks. A human can use their judgment, whereas an AI (at the moment and for the foreseeable future) will interpret things in the most literal way.

For Example: QAs will work with requirements that might be written by business analysts or a project team. If a human is reading these and the requirement doesn't quite make sense, they would be able to interpret that and go back to the person who wrote the requirements for clarification. If an AI was writing tests based on those requirements, it might end up creating a test that doesn't match the original intent. I think it will be a while before AIs are able to use judgment in that kind of way.

What makes QA engineering a good tech career path now and in the near future?

Ironically, AI! While there's all this fear about AI taking jobs, the reality is that there will be more projects that need QA testers. Also, AIs aren't perfect. There have been many cases where AIs have been tricked into giving outputs that it was programmed not to give. 

There will be so many more QA jobs involved in this kind of process. It may evolve to be called something else, but for now at TripleTen, we're teaching the skills that will allow people to progress into these up-and-coming careers.

How could AI help QA engineers do their jobs better

We expect that AI will help people do their jobs better and ideally more enjoyably as well! One of the first places where we see potential for AI to help QA testers is with writing automation scripts. QA engineers are not software engineers; it's a different skill set. At the moment, writing scripts requires some of the skills of a software engineer, which can be tricky. The hope is that AI will help QA engineers write these automation scripts and relieve that burden.

Are there current AI tools that QA testers can employ to make them more effective and valuable in their organizations? 

In terms of specific tools, we're going through a period of accelerated development with tools being generated so quickly. It seems like there’s a new AI program that comes out every week, for example, Google's Bard is now also able to write code! The first place where I expect to see AI tools in QA is code creation – it's already happening with software engineers. ChatGPT and GitHub Copilot are two examples of tools that can be used to turn natural-language prompts into code. It's not strictly specific to QA but some QA engineers will write code for automated testing and it's likely that they'll be using and experimenting with these tools. In terms of companies integrating this into their work, it will take time to adjust and build these processes into their actual workflows. 

However, many QA engineers write automated test scripts. ChatGPT, GitHub Copilot, and Google Bard are examples of tools that QAs can use to turn natural-language prompts into code. These are Large Language Models (LLMs), which have been causing all the excitement in the media. We're sure many QAs are already experimenting with these tools and we’ll start to teach them as they filter through to the jobs market. 

The careers team at TripleTen has started recommending adding ChatGPT for the job search. They look at people's resumes and cover letters, analyze job postings, and write about AI in the TripleTen blog. Our careers team will be the first to pick up on the demand for AI skills. We constantly get feedback from them so that our curriculum can evolve with the job market. We don't want to overwhelm students so we focus on the skills they need to land a job now. 

What do students learn in the TripleTen QA Engineering Bootcamp?

The course is structured in a linear fashion, starting with the fundamental principles, explaining the different types of testing and what that type of testing involves. There are many different ways students could go in their career so we try to cover everything at TripleTen. Typically, further down the road, a student will specialize in a particular type of testing, either manual QA testing or automated QA testing. 

Manual testers do everything by hand, which is why we cover the core principles at the beginning. At the end, we add in the automation content, which not everyone will need to know but is good to have on their CV in case they see a job they're interested in that requires it. We also teach JavaScript, since we've seen it required for many QA jobs. 

For TripleTen students who graduated a year or two ago, what are your recommendations to them on staying up-to-date with these new AI technologies?

One great thing about the TripleTen courses is that students still have access to the course after they graduate. Any time we add something in the future about AI, alumni will have access to that. Our hope is that all of our students will be able to get employed quickly and that by the time they need that information their career path will have already led them to what's required of them. I think it's important for not just people who have finished our courses but anyone in general to be kept up-to-date with AI as it shapes the workplace. 

Find out more and read TripleTen reviews on Course Report. This article was produced by the Course Report team in partnership with TripleTen.

About The Author

Jess Feldman

Jess Feldman

Jess Feldman is an accomplished writer and the Content Manager at Course Report, the leading platform for career changers who are exploring coding bootcamps. With a background in writing, teaching, and social media management, Jess plays a pivotal role in helping Course Report readers make informed decisions about their educational journey.

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