Artificial intelligence is transforming software development. As a result, it is also transforming software testing. Generative AI can analyze requirements, prepare test cases, generate test data, summarize results, and assist testers with numerous tasks that were previously performed primarily by hand.
This raises an intriguing question for software testers: What tasks will actually remain for humans if AI can support more and more of the testing work?
The answer is less dramatic than some headlines might suggest. Generative AI does not make professional software testers obsolete. Rather, it shifts the focus of their work.
Anyone who wants to use AI effectively in testing must continue to understand what constitutes good quality, what risks exist, and when an AI-generated result is actually reliable.
As a result, traditional testing expertise is increasingly evolving into a combination of testing, AI expertise, and critical evaluation.
What Does Generative AI Mean in Software Testing?
Generative AI can generate new content based on existing information. In software testing, for example, this means that a large language model analyzes requirements or user stories and uses them to develop suggestions for test cases.
It can prepare test data, identify potential edge cases, revise existing test cases, or assist in formulating acceptance criteria.
Generative AI can also play a role in analyzing bug descriptions, documentation, or the preparation of automated tests.
This sets the approach apart from traditional test automation.
In traditional automation, the steps a tool will perform are defined in advance. Generative AI, on the other hand, can generate new suggestions based on natural language, code, or other information, thereby assisting testers as early as the analysis and design phases.
This opens up new possibilities. However, it also brings new challenges for quality assurance.
AI is changing test preparation in particular
A significant portion of professional testing begins long before the actual testing takes place.
Requirements must be understood, risks assessed, and appropriate tests developed. It is precisely during these early phases that generative AI can provide significant support.
For example, a tester can have a user story or a requirement analyzed and receive suggestions for possible test conditions.
The AI can point out missing information or formulate questions that should then be clarified with subject matter experts and developers.
Various test scenarios can also be prepared more quickly.
As a result, the tester's role is shifting from one focused solely on development to one that involves more verification and oversight.
The tester does not necessarily have to come up with every idea on their own. However, they must be able to determine which of the ideas generated are useful and which are not.
Test cases can be generated more quickly, but they should not be adopted without being reviewed
One of the most obvious applications of generative AI is the creation of test cases.
Requirements, user stories, or other components of the test basis can quickly generate numerous suggestions.
This can be particularly helpful at the beginning of a test analysis.
However, more test cases do not automatically mean better tests.
An AI may overlook important constraints, misinterpret requirements, or generate tests that sound plausible but do not actually provide any additional insights.
This reflects a fundamental shift in the role of the tester.
The core competency is increasingly shifting from simply generating test cases to assessing their quality.
Which tests are relevant? Which risks are actually covered? Where are important scenarios missing? Where are there simply variations of the same test?
Generative AI speeds up the creation process. However, the responsibility for developing a sound testing strategy remains with humans.
Test data can also be generated more quickly using AI
Test data is an essential part of many test scenarios.
Generative AI can help prepare various datasets, combinations, or edge cases, thereby reducing the effort required to create test data.
This is particularly interesting when a large number of different situations need to be considered.
At the same time, new questions arise.
Is the data generated actually suitable for the test? Does it inadvertently contain problematic patterns? Is sensitive information being used? And does the data realistically represent the required scenarios?
AI can make it easier to generate test data.
However, this does not relieve testers of the responsibility to verify whether this data aligns with the specific test objective.
Prompt engineering is also becoming a skill for testers
Those who use generative AI in software testing often communicate with the system via prompts.
This makes prompt engineering relevant for software testers as well.
A very general prompt such as “Create test cases for this requirement” can yield a useful result. However, this is often not sufficient for professional testing.
The more clearly the test objective, context, expected coverage, and framework are defined, the more effectively AI can provide support.
As a result, testers increasingly need the ability to formulate tasks for AI systems in a structured manner.
It's not about memorizing complicated prompt formulas.
What matters most is that the tester himself understands which testing problem needs to be solved.
Only those who know the goal can determine whether the AI's response is helpful.
Prompt engineering thus becomes an extension of traditional testing expertise.
AI can critically evaluate requirements
Software testing doesn't start only after the code is finished.
Unclear or contradictory requirements are among the most common causes of problems down the line. Generative AI can assist testers as early as the test case analysis phase.
For example, an AI system can look for ambiguities, identify missing information, or formulate questions that should be clarified as the project progresses.
This creates an interesting connection between requirements engineering and testing.
AI does not replace dialogue with stakeholders.
However, it can help identify more quickly the situations in which this conversation is necessary.
This can be particularly valuable for testers, because it allows quality to be addressed earlier in the development process.
The tester is increasingly taking on the role of an evaluator of AI results
Generative AI produces results very quickly.
That is precisely where both its strength and its greatest challenge lie.
An answer can be convincingly worded yet still be factually incorrect. A test case can look professional yet still overlook an important aspect of the application.
Generative AI is therefore giving rise to a new form of quality assurance within the field of quality assurance.
Testers no longer just test software.
They are also increasingly required to evaluate the quality of the AI tools they use in their own work.
This significantly changes our day-to-day work.
Instead of writing every test case from scratch themselves, testers can generate suggestions and then evaluate, refine, and prioritize them.
This does not make critical thinking any less important.
It's becoming more important.
Hallucinations Pose a Quality Risk in Testing
Generative AI can generate information that seems plausible but is factually incorrect.
In the general context of AI, this behavior is often referred to as a hallucination.
In software testing, this can result, for example, in a system assuming the existence of functions that do not actually exist, misinterpreting requirements, or inventing expected results.
Anyone who accepts such answers without verifying them may be carrying errors directly into the test data.
That is why, when using generative AI, a new control question must become second nature:
How do we know that this AI result is correct?
This question fits very well with the mindset of professional testers.
After all, skepticism toward assumptions is already an essential part of good testing practice today.
Generative AI does not, therefore, change the fundamental concept of testing. It merely provides another source of results that must be critically evaluated.
Bias can also affect test quality
Another challenge arises from potential biases in AI-generated results.
If an AI system places greater emphasis on certain typical usage scenarios than others, this can also affect the tests it suggests.
As a result, certain user groups, edge cases, or unusual situations might not be taken into account as fully.
Testers should therefore not assume that AI automatically ensures complete coverage.
Human experience, in particular, can help us take into account situations that are not immediately apparent from the available information.
Generative AI expands the tester's capabilities.
However, it does not replace the individual's responsibility to take a balanced and risk-oriented approach.
Data Protection and Information Security Remain Critical
Those who use AI for software testing often work with information from real-world development projects.
These may include requirements, source code, error descriptions, system information, or test data.
Not all of this information should be entered into just any AI application without further consideration.
This means that, in addition to testing expertise, the ability to handle information securely is also required.
Testers need to know which AI tools are approved for use within the company, what data they are permitted to use, and what internal guidelines apply.
A technically excellent prompt is of little value if it processes information that does not belong in the system being used.
The professional use of generative AI therefore begins not only with the question of what is technically possible, but also with the question of what is responsible within the specific business context.
Generative AI can also support test automation
Another interesting area is test automation.
Generative AI can, for example, help with understanding code, developing suggestions for test scripts, or revising existing automation.
However, the same applies here: A generated script is, at first, just a suggestion.
It must be understood, tested, and integrated into the existing test architecture.
For test automation engineers in particular, AI can help them get up to speed on certain tasks more quickly. At the same time, however, an understanding of architecture and the ability to evaluate technical aspects are becoming increasingly important.
As AI generates code more quickly, the question of whether this code is maintainable in the long term and technically sound becomes all the more important.
Speed alone is not an indicator of quality.
How does this change the role of the software tester?
Generative AI is shifting work away from certain repetitive tasks and toward evaluation, management, and quality assurance.
As a result, testers are increasingly becoming people who make targeted use of AI support and verify its results.
That doesn't mean that traditional skills are disappearing.
Knowledge of testing methods, risks, test design, quality, and software development remains the foundation.
A new skill being added is the ability to use generative AI professionally as a tool.
A good tester of the future, therefore, does not need to become an AI developer.
However, he should understand what generative AI is capable of, where its limitations lie, and how its results can be meaningfully integrated into the testing process.
More AI does not automatically mean fewer testers
If AI can create test cases, generate data, and prepare documentation, one question naturally arises: Will there be a reduced need for software testers in the future?
A blanket answer wouldn't make much sense.
Generative AI can significantly speed up certain tasks. At the same time, however, expectations regarding software quality and the complexity of digital systems are rising.
The AI-generated results themselves must also be evaluated.
This changes not so much the fundamental necessity of testing as the way in which testing is carried out.
Routine tasks can be supported to a greater extent. As a result, human expertise shifts toward more complex tasks such as risk assessment, test strategy, interpretation, and quality decisions.
"How do I create this test?" often leads to the question:
"Is this proposed test really the right one?"
Generative AI should not be implemented only on a case-by-case basis
Individual testers often start experimenting with AI tools on their own.
That can lead to valuable experiences.
In the long term, however, companies should take a structured approach to its use.
We need clarity on the tasks for which generative AI should be used appropriately, which tools are permissible, and how results are verified.
It is equally important to share experiences from individual teams.
A good prompt, a useful approach, or a recognized weakness of an AI tool shouldn't be kept to just one person.
As a result, the use of generative AI is evolving from a personal productivity experiment into an integral part of professional test organizations.
CT-GenAI provides a structured introduction
This is exactly where the ISTQB Certified Tester – Testing with Generative AI (CT-GenAI) comes into play.
This certification program is designed for software testers, test analysts, test managers, test automation engineers, and other professionals who want to use generative AI specifically for software testing tasks.
It's not just about being able to use an AI tool.
The focus includes, among other things, the fundamentals of generative AI and large language models, prompt engineering in testing, risk management, and the integration of AI-supported approaches into existing testing processes.
In this way, CT-GenAI combines proven testing expertise with the new possibilities offered by generative AI.
It is important to make a clear distinction here: CT-GenAI focuses primarily on using generative AI for testing.
On the other hand, those who focus specifically on how AI-based systems are tested themselves are more involved in the field of ISTQB AI Testing.
This distinction helps companies and testers choose the right certification for their specific task.
Good testers are becoming more important—not less important—thanks to AI
Perhaps the most significant change brought about by generative AI is that the number of possible outcomes increases enormously.
AI can quickly generate numerous test ideas, suggest variations, and process information.
But someone still has to decide which of them are relevant.
This is exactly where the value of professional testers becomes apparent.
Those who understand risks, are familiar with quality goals, and consider the perspectives of different users can use generative AI much more effectively than someone who simply lets it produce results.
AI thus does more than just speed up testing.
It also highlights the importance of sound judgment.
Conclusion: Generative AI is changing testing, but not the goal
The goal of professional software testing remains the same: to demonstrate quality, identify risks, and build trust in digital products.
Generative AI, however, is changing the tools used to achieve this goal.
Test cases can be prepared more quickly, requirements analyzed, test data generated, and automation tasks supported. At the same time, new risks arise from erroneous AI outputs, bias, data privacy, and the uncritical acceptance of results.
As a result, the role of the software tester is also evolving.
In the future, testers will not only have to critically evaluate software; they will also need to be able to assess when AI support is helpful and when its output itself becomes the subject of human scrutiny.
Generative AI does not, therefore, replace the expertise of good testers.
In many places, it actually makes this expertise even more visible.
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To learn how companies can balance rules, accountability, and oversight when using artificial intelligence, see the previous post:
"AI Compliance Explained Simply: What Companies Need to Consider When Using AI"
Training Tip: ISTQB Certified Tester – Testing with Generative AI at SERVIEW
If you want to use generative AI in a targeted and professional manner in software testing, the ISTQB Certified Tester – Testing with Generative AI (CT-GenAI) training course at SERVIEW is the right next step.
This training provides hands-on instruction on how to use large language models and generative AI throughout the testing process. You will explore prompt engineering, AI-assisted creation and optimization of test cases and test data, as well as risks such as hallucinations, bias, and data privacy.
This allows you to expand upon traditional ISTQB competencies with skills that are becoming increasingly important for modern AI-assisted testing.
Learn more:
ISTQB Training Courses at SERVIEW
