Software Testers: Why Your Job is Safe from AI (And How to Level Up)

As a software tester, I’ve found myself pondering a question that might be on your mind too: “Will AI eventually take my job?”.

It’s understandable why many of us might feel this way. Each day seems to bring news a new AI tool that promises to automate yet another part of the software testing process. With platforms like ChatGPT, Testim, and Mabl automating more of our workflows, I’ve caught myself contemplating the future of human testers.

But should we be worried? Will AI replace human testers completely?

Tldr; the straight-up answer is no!

But why am I so sure and if it won’t replace testers, where does that leave us? But before we discussed that, let’s briefly address how AI is affecting software testing today.

 AI’s Current Impact on Software Testing

Without a doubt, AI testing tools are changing the face of software testing. With faster test cycles, broader coverage, and continuous testing in CI/CD pipelines, these tools have made a significant impact. Generative AI, in particular, can be quite effective in user interaction and the creation of test data, helping teams reduce time needed for regression testing and value delivery.

At face value it seems like AI would gradually evolve until it eliminates human involvement in testing. But it AI won’t take over human testers, what should we expect?

 What the future holds?

Given the current state of AI technology, it can’t completely replace software testers now and in the foreseeable future.

This is because software testing is all about a holistic approach, thought process, critical thinking, applying your capabilities, understanding requirements, and writing test cases. While you can get help from ChatGPT in writing test cases, you still need to set up the environment, understand exactly what you’re doing, and grasp the integrated environment execution.

All of that is not fully achievable with AI tools in the same state we are in at the moment, and even in the future, human intelligence cannot be replaced by AI tools.

For instance, exploratory testing, a process in which testers go through software without predefined scripts in an effort to discover hidden issues, relies mostly upon human instinct and creativity. Testers use their instincts and creativity to think outside the box and discover hidden problems that AI tools might miss. Human testers will try all sorts of unexpected things to see if something goes wrong. Human testers are also crucial when it comes to testing whether the software is fair and accessible to everyone and how it impacts people in general. These are things AI cannot fully understand and handle by itself.

Testers should view AI as something that can further their capabilities, so they have time to focus on higher levels of tasks which may require human judgment and expertise, rather than considering it to be a threat.

 Practical Steps on How Testers Can Embrace AI and Develop New Skills

I can do things you cannot; you can do things I cannot; together, we can do great things. —Mother Teresa

So, if AI isn’t going to replace us, where does that leave us? Well, I believe we’re heading towards a new paradigm, one where human testers and AI tools collaborate rather than compete. This shift in how we approach software testing is already transforming roles in the field.

While it’s true that there might be a decrease in demand for purely manual testing, new opportunities are emerging. Roles like “test development strategist” and “AI supervisor” are becoming more relevant. In these positions, testers can oversee AI’s work, ensuring that it runs smoothly and accurately, while also focusing on more strategic tasks that require human judgment and creativity. The evidence is already here: more companies are integrating AI-driven tools into their workflows, and testers are evolving to handle more complex, creative challenges, leaving repetitive tasks to AI.

In my experience, this collaboration allows me to focus on the bigger picture, designing better strategies, improving the quality of testing, and working on more meaningful, impactful tasks. AI handles the tedious stuff, and I get to focus on what really matters.

In order to remain relevant as software testers, testers will have to embrace AI technologies and merge them within their workflow. The following are some steps to take that are more practical to get started:

1. Learn the Tools: Start by learning about AI-driven testing tools available on the market. Understanding how these tools work, coupled with their strengths and limitations, will help you use them effectively in your testing process.

2. Automate the Repetitive: Start by identifying time-consuming tasks that AI can handle for you. For example, let’s say your team spends hours on regression testing after each new deployment. You can use tools like Testim or Mabl to automate these repetitive tests, allowing AI to run them in the background while you focus on tasks that require human insight, like exploratory testing or strategizing for edge cases. This frees up time and reduces human error in repetitive tasks.

3. Be Informed: A great way to stay informed and up to date in the world of testing is by subscribing to industry newsletters or following influential testing blogs such as MagicPod blog, also attending webinars and conferences. Regularly staying informed and up to date, will help you adapt to new tools and methodologies as they emerge.

4. Focus on Strategy: In the absence of routine tasks, focus on your strategy in designing effective tests. The more profound your understanding of the software and the base of users, the more risks you will potentially be able to understand in order to design more effective and targeted tests with AI.

5. Develop Your Analytical Mind: AI tools generate huge amounts of data. Learning how to interpret this data, find patterns, and infer meaningful insights will be key. Such analytical skills will let you drive your decisions with data and impact the overall quality of the software.

6. Learning AI and Machine Learning Concepts: Even the fundamentals of artificial intelligence and machine learning are very useful. Once you have this knowledge, you will be able to understand how the AI tools work and how you can effectively use them in your testing process.

 Summary

The biggest lesson learned is clear: AI is not a competitor but a partner. Testers who realize this and position themselves to work with AI will not only secure a place in this industry but also lead the way in shaping the future of software testing.