Maria Homann
Since the introduction of ChatGPT and other AI applications like Google Gemini and Microsoft Copilot, artificial intelligence has become much more widely adopted. Naturally, some people have been wowed by its capabilities and, with that, have become afraid of losing their jobs. But when it comes to software testers' jobs, there’s no reason to be afraid. Let’s explore why in this blog.
The hype cycle and the reality of AI
People have a tendency to overhype technology’s abilities in the beginning while underestimating its potential in the long term. This tendency is researched and documented in Gartner’s hype cycle, which depicts the typical pattern of how new technologies and innovations are accepted by the population.
And this pattern is already proving to be true for AI. Initially, AI’s capabilities were seen as a threat to many jobs, including those in software testing. But while many people are impressed by the technology’s capabilities at first glance, they don’t necessarily see its limitations. But there are plenty of limitations.
For example, While ChatGPT produces well-formulated text at high speed, and for that reason is used by many to produce copy at mass, regular users of ChatGPT are now starting to recognize its default language patterns, making ChatGPT produced text easily recognizable to these users.
This doesn’t mean that Generative AI tools can’t or shouldn’t be used as a part of workflows and process optimization. It just means that users should be cognizant of its flaws.
AI’s impact on job roles and essential skills
History has shown that while technology changes job roles, it rarely eliminates them entirely. Instead, it transforms them, creating new opportunities and challenges.
For instance, when test automation was first introduced into software testing, there was a fear that manual testers would become obsolete. That wasn’t the case - however, organizations that are successfully adopting automation today have a much lower need for the manual testing tasks that involve highly predictable, repetitive work such as regression testing. What’s needed now are testers who know what to automate, what not to automate, and how to build and follow good testing practices.
AI will have the same effect. Understanding AI will be crucial for future success in QA. This involves two key areas:
1. Utilizing AI for productivity
Learning how to use AI in tools and techniques to improve productivity and efficiency in day-to-day work is essential. Testers should understand both the possibilities and (perhaps more importantly) the limitations of these tools to adopt the right use cases effectively.
2. Testing AI systems
As organizations integrate AI into their techstacks, testers face a new era of quality assurance. For example, How do you test a GenAI chatbot? How will you know what people will ask it and how do you make sure it’s going to respond how you want it to? Testing generative AI can be a bit of a Pandora’s Box. These are the challenges testers will encounter more frequently in the future.
Testing AI systems comes with unique challenges, such as dealing with non-deterministic outputs, ensuring the AI model's fairness and bias, and validating the AI’s decision-making process. Testers need to develop new methodologies and tools to address these challenges effectively. This might include generating synthetic data for testing, deploying adversarial testing, and implementing continuous monitoring and learning systems to ensure AI systems remain reliable and accurate over time.
People who identify these skills and actively work on developing them will not only secure their jobs in the long run, they will likely also be getting a better paycheck.
And while some skills are growing in demand, others are on the decline.
According to the State of Test Automation report:
- In 2023, 50% saw programming skills as essential in testing. By 2024, that percentage dropped to 31%.
- Test experiment and design skills were perceived as highly important last year (63%) but dropped to below half this year, to 30%.
- Conversely, skills in AI/machine learning have increased from 7% in 2023 to 21% in 2024. This is a skill that is expected to continue to grow in demand.
- Communication skills are at the top of the list of skills that testers need to thrive in today’s testing industry.
- Right after communication comes functional testing skills (47%) and test automation patterns, principles and practices skills (46%).
What’s causing the shift in demand in these skill sets?
One factor that is definitely influencing the numbers is the changing landscape of tools available. No-code test automation tools are on the rise, making traditional programming skills and roles like 'Selenium Test Engineer' less sought after.
Instead, testers need to understand how to build and manage tests that accurately identify risks in applications, especially new ones like AI systems.
Generally speaking, organizations are moving away from the need for simple, entry-level skills towards a demand for critical and creative thinking and advanced skills. Testers will need to know “what good looks like” but won’t need to practice “how to get there”. It’s similar to using a calculator: you need to know what to input and how to validate the output, but you don't need to do the middle math. This validation part is even more crucial in the context of generative AI since these systems aren’t as reliable (yet) as a calculator.
As the technical requirements evolve, so too do the soft skills required. Communication, critical thinking, and problem-solving become even more valuable. Testers must be able to understand applications and processes deeply, articulate complex issues to non-technical stakeholders and work collaboratively. This human touch is irreplaceable and underscores the importance of testers in the AI-driven future.
AI-assisted vs. fully autonomous testing
You might have heard the term ‘autonomous testing,’ the idea that testing can be completed without any human intervention. Despite the hype, we are far from reaching this autonomous testing nirvana.
Currently, some testing tools can perform tasks such as self-healing scripts, where the AI adjusts the tests when there are minor changes in the application. However, these tools are not yet sophisticated enough to handle the complexity of most real-world applications without human oversight. The quality of AI-generated tests often needs human validation to ensure accuracy and relevance.
Today’s ideal scenario is more like a tight-knit collaboration between humans and AI. The human provides the input, the AI does the middle work, and then it returns to the human for validation.
In other words, humans shouldn’t work without AI and AI shouldn’t work without humans. Instead, humans and AIs should work together, creating a mutually beneficial relationship. 2+2=5, if you will.
This collaborative approach ensures that while AI handles repetitive tasks, humans focus on strategic, high-level testing activities that require creativity and critical thinking.
Conclusion: Embrace change and get in gear for an AI future
Evolving your skills as a tester will be crucial for your future career. Keep your eyes open to AI and don’t resist the change - it is here to stay. Those who don’t adopt it will be left behind.
Stay up to date with use cases and continue to learn how to use AI effectively. The role of the tester will remain important, perhaps even more than ever, as organizations increasingly rely on technology. An eye for quality is essential for businesses to create responsible and reliable solutions for their customers.
QA will continue its journey from the backroom to the boardroom, and AI will only help it do so, as long as testers continue to learn and adapt.
AI is not here to replace testers but to enhance their capabilities. By embracing AI, testers can ensure their roles remain vital and evolve with the technological advancements. The future of QA is bright for those who are willing to grow and adapt alongside AI.
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About the author
Maria Homann has 5 years of experience in creating helpful content for people in the software development and quality assurance space. She brings together insights from Leapwork’s in-house experts and conducts thorough research to provide comprehensive and informative articles. These AI articles are written in collaboration with Claus Topholt, a seasoned software development professional with over 30 years of experience, and a key developer of Leapwork's AI solutions.