AI in Software Testing 2026: How Artificial Intelligence Is Changing Testing—and Where It Reaches Its Limits

Testing with AI and testing AI: two different tasks

The terms sound similar, but they require different skill sets.

This distinction is now also reflected in the ISTQB: CT-GenAI addresses the use of generative AI in testing. CT-AI v2.0 focuses entirely on testing AI-based systems and explicitly includes generative AI and large language models. The new CT-AI syllabus has been streamlined from eleven to seven chapters and restructured to align with the machine learning lifecycle.

In real-world projects, teams increasingly need both perspectives.

Where AI Really Helps in Software Testing Today

The question “Which AI tool should we use?” is often asked too soon.

A more interesting question to start with is:

What task do we want to solve better?

Developing test ideas and test cases

Generative AI can analyze requirements, user stories, and acceptance criteria and derive test conditions and test ideas from them.

Here’s an example: For a discount logic, the model shouldn’t simply “generate 30 test cases.” Instead, it’s given the business rules and the task of examining equivalence classes, threshold values, and relevant combinations.

This can save a lot of preparatory work.

However, the crucial questions remain with the testers:

Did the model understand the requirement correctly? What risks are missing? Did it assume prerequisites that weren’t described anywhere?

This is precisely why AI-supported testing often begins one step earlier: with the quality of the requirements.

AI can reveal gaps and contradictions. But it can just as easily translate unclear requirements into unclear test cases.

Generating Test Data

Generative AI is well-suited for generating synthetic test data and quickly converting variants into the required formats.

For an international checkout, for example, combinations of countries, addresses, currencies, payment methods, and edge cases may arise.

What AI doesn’t automatically know: which combinations are particularly critical from a business perspective, which data must be representative, and which data protection rules apply.

Here, too, test design remains more important than mere generation.

Supporting Test Automation

Coding assistants and coding agents can generate, explain, and customize test code—and, increasingly, execute it autonomously.

This shifts part of the work from writing to testing, evaluating, and making decisions.

And this is precisely where a tool-related issue quickly becomes a consulting issue:

What changes is an agent allowed to make on its own? What quality gates apply? Who reviews AI-generated code? What access rights does the system receive? Where is human approval required?

The more AI becomes involved in development processes, the more important architecture, governance, and clear responsibilities become.

Analyzing Errors and Test Results

Another key area of application is analysis.

LLMs can summarize logs, group similar errors, explain test results, or link possible causes to changes in the system.

AI-powered testing platforms now integrate such functions directly into test management and test automation.

The added value here isn’t that AI suddenly “finds all the bugs.”

What’s more interesting is that people spend less time gathering information—and more time on their technical evaluation.

AI Tools in 2026: Choose Based on the Task, Not the Hype

A “Top 10 List of the Best AI Testing Tools” is usually outdated before the article is even published.

It makes more sense to categorize them by task:

The more important questions are:

What problem are we trying to solve?
What data does the AI need?
What data is it allowed to access?
Which systems is it allowed to modify?
How do we verify its results?
And how can we tell that the new process is actually better?

These are exactly the questions that should be answered before a major rollout.

Prompting for Testers: Structure, Not Magic

Good prompting isn’t about magic phrases.

The current ISTQB CT-GenAI syllabus describes six components of structured prompts: role, context, instruction, input data, constraints, and output format.

For day-to-day work, these can be easily summarized in four questions:

1. Who is working in what context?

Here, we combine role and context. What perspective should the model adopt? And what does it need to know about the system, the domain, and the task?

Example: “You work as a test analyst for a B2B online store. Orders over 10,000 euros require additional approval.”

2. What specifically needs to be done?

This is the instruction. It should clearly and as specifically as possible describe the task the model is supposed to perform.

Example: “Derive test conditions using threshold analysis and decision tables.”

3. On what basis and within what limits?

This is where input data and boundary conditions come together.

Which user stories, acceptance criteria, test cases, screenshots, or code snippets should the model use? And what should it explicitly not do?

Example: “Use only the following user story and its acceptance criteria. Do not invent any additional business rules. Mark missing information as open questions.”

4. What should the result look like?

The output format makes expectations more verifiable.

Example: “Provide a table with test conditions, risks, test data, and expected results.”

Put together, this results in, for example:

You work as a test analyst for a B2B online store. Orders over 10,000 euros require additional approval. Based on the following user story, derive test conditions using boundary value analysis and decision tables. Do not invent missing business rules, and mark any ambiguities as open questions. Present the result as a table containing test conditions, risk, test data, and expected results.

The key point is not to make every prompt as long as possible.

The structure forces us to consciously formulate the task, initial situation, constraints, and expectations.

The CT-GenAI syllabus supplements this basic structure with techniques such as prompt chaining, few-shot prompting, and meta-prompting, among others.

And as with traditional testing, the following applies:

A well-formulated prompt does not replace the need to verify the result.

AI Agents Put to the Test: When Answers Become Actions

A chatbot responds.

An agent can take action.

It can read files, examine repositories, modify code, run tests, and select the next step based on the result.

This enables workflows such as:

Requirement → Test idea → Test code → Test run → Error analysis → Change proposal

That’s powerful. At the same time, it introduces a new dimension of quality.

We no longer need to merely verify what an AI system says, but also what it does.

This includes questions regarding permissions, secrets, network access, sandboxing, logging, and approvals.

And agents are transforming collaboration.

When an agent takes on tasks that were previously performed by developers or testers, teams need to redefine:

Who reviews which result?
What will a code review mean in the future?
When do we need a dual-review process?
Which decisions will deliberately remain in human hands?
Which skills will become more important?

That’s why AI implementation rarely works in the long term as a purely bottom-up tool initiative.

Teams need the freedom to experiment—but they also need shared guidelines, learning loops, and clarity regarding responsibility.

Testing AI systems: When the expected result is no longer clear

With traditional software, we often expect a clear relationship:

Input X leads to Output Y.

Generative AI makes this more complicated.

An LLM can provide different acceptable answers to the same question. At the same time, an answer can be convincingly worded yet still factually incorrect.

Testing therefore requires several types of test oracles and evaluation methods:

• deterministic tests for unambiguously measurable properties,
• curated test and reference data,
• repeated test runs,
• appropriate quality metrics,
• exploratory testing,
• red teaming,
• testing of data, retrieval, and tools,
• human evaluation.

This is exactly where ISTQB CT-AI v2.0 comes in. The current version focuses entirely on testing AI-based systems. The syllabus follows the ML lifecycle and covers, among other things, input data, models, ML development, as well as dedicated content for Generative AI and LLMs.

Can one AI evaluate another AI?

Yes. In “LLM-as-a-Judge,” a language model evaluates the output of another model based on predefined criteria.

This can be very helpful when dealing with large volumes of generated responses.

But the judge is also a model.

Where something can be evaluated deterministically, we should evaluate it deterministically. Where an LLM evaluates, its evaluations should be calibrated against expert-reviewed examples and human judgments.

In short: AI can be part of the test oracle. It should not replace the test oracle without verification.

What the EU AI Act 2026 Means for Software Testing

Starting August 2, 2026, additional key provisions of the EU AI Act will take effect. As of that date, transparency requirements under Article 50 will also apply to certain AI systems. This includes, for example, ensuring that users of certain interactive applications can recognize that they are interacting with AI. Labeling requirements also apply to certain AI-generated or manipulated content.

The timeline for high-risk AI has since been adjusted. The relevant rules for certain autonomous high-risk systems under Annex III will take effect on December 2, 2027. For high-risk AI integrated into certain regulated products, the effective date is August 2, 2028.

For software testing and quality engineering, the implications are particularly noteworthy.

Risks, data, robustness, security, transparency, and human oversight must be considered more systematically. Testing can provide an important part of the evidence for this:

What was tested? With what data? Against what quality criteria? What limitations were identified? How does the system behave in the event of errors or uncertainty?

This brings quality into the lifecycle even earlier.

Compliance does not begin shortly before an audit. It begins with product decisions, requirements, architecture, and quality strategy.

This section is not a substitute for legal advice.

Where AI Reaches Its Limits in Testing

AI can be surprisingly convincing when it’s wrong.

This leads to some very practical risks.

Hallucinations: LLMs can invent requirements, APIs, or business rules.

Automation Bias: The more often a tool delivers good results, the greater the risk that people will scrutinize the next result less critically.

Data Protection and Information Security: Requirements, logs, source code, and customer data should not automatically be included in every model.

Agent risks: Incorrect text is annoying. An incorrect action with write permissions can have entirely different consequences.

Changing systems: Models, prompts, retrieval data, and connected tools change. This can also alter an application’s behavior.

And finally:

More generated tests do not automatically mean higher quality.

If AI generates 1,000 test cases, we still need to know which ones actually provide insights.

Professional testing expertise therefore does not become any less important because of AI.

It becomes even more important.

AI in testing is rarely just a testing issue

A team can start with a single AI tool.

But as soon as this is to become a permanent way of working, AI quickly impacts the entire software lifecycle.

Product and requirements: What problem are we trying to solve? How do we define success and acceptable risks?

Architecture and Development: Where are models and agents integrated? What technical limitations apply?

Quality Engineering: What evaluation methods, testing strategies, and quality gates do we need?

DevOps and Operations: How do we monitor quality, costs, and changes in system behavior?

Security and Resilience: What happens in the event of failures, tampered inputs, or erroneous actions?

And finally:

Organization: Which tasks and roles are changing? Where will decisions be made in the future? How do teams learn from one another? And how do we prevent each team from inventing its own rules for AI?

This makes AI adoption part of organizational development as well.

Not necessarily as a major change program.

It often starts more pragmatically: with a suitable use case, a pilot team, and clear success criteria. Then we look not only at the technology but also at the way work is done.

What has actually become faster?
Where is new review work arising?
Which decisions still require human input?
What skills are missing?
What guidelines are helpful?
And which of these should be applied to other teams?

Learn, adapt, standardize, scale.

This is usually more effective than first writing a company-wide set of AI rules and then hoping that it works in practice.

At trendig, we therefore look beyond just testing. Our consulting services combine product and innovation work, requirements engineering, software architecture, development processes, quality engineering, digital resilience, transformation, as well as DevOps and continuous delivery.

A key focus is explicitly on creating solutions that fit the teams and the organization. trendig’s current positioning identifies clear decision-making paths, shared vision statements, and effective interfaces between product, design, development, and quality assurance as part of sustainable innovation.

That’s why we don’t automatically start by asking: Which AI tool do we want to implement?

Instead, we start with:

“What do we want to improve—and how will we know when it has improved?”

Depending on the starting point, this can lead to a workshop, a pilot project, a quality strategy, a change in the development process, coaching, or training.

The goal is not to use as much AI as possible.

The goal is better software development with manageable AI support. 

Software Engineering Consulting at trendig

Professional Development: Testing with AI—and Learning to Test AI

Tools change rapidly.

Methods and skills last longer.

That’s why we also distinguish between different goals in our continuing education programs.

ISTQB® Certified Tester – Testing with Generative AI (CT-GenAI)

For those who want to use generative AI professionally for testing activities, the ISTQB CT-GenAI offers a structured introduction.

At trendig, the training lasts 3 days.

The current syllabus covers, among other topics:

• Fundamentals of GenAI and large language models,
• prompt engineering for testing tasks,
• Risks such as hallucinations, bias, and data privacy,
• LLM-powered testing infrastructures and agents,
• as well as the organizational implementation of Generative AI in testing.

The last point is particularly important: A new technology does not automatically translate into a new way of working. Teams must gain experience, evaluate results, and develop common approaches based on those findings. ISTQB also explicitly addresses organizational adoption and competency development in the current CT-GenAI.

ISTQB CT-GenAI

AiU Certified GenAI-Assisted Test Engineer

The AiU Certified GenAI-Assisted Test Engineer program is particularly practice-oriented.

At trendig, the training also lasts 3 days.

The focus is on the practical application of Generative AI in testing activities—for example, in requirements reviews, test design, test data, and the communication of defects.

The training is therefore particularly suitable for testers who want to integrate GenAI directly into their daily work.

AiU Certified GenAI-Assisted Test Engineer

ISTQB® Certified Tester AI Testing – CT-AI v2.0

On the other hand, those who want to test AI-based systems themselves need a different focus.

For this purpose, we offer the ISTQB CT-AI v2.0 as a 4-day training course. The training overview and current schedule list CT-AI as a four-day course.

CT-AI v2.0 is the current version of the ISTQB certification for testing AI-based systems. Among other topics, it covers data quality, machine learning models, neural networks, AI-specific quality characteristics, and the testing of generative AI and LLMs.

This results in three different perspectives:

CT-GenAI: Methodically applying generative AI in testing.
AiU GenAI-Assisted Test Engineer: Using GenAI in a highly practical way in daily testing work.
CT-AI v2.0: Professionally testing AI-based systems.

And because AI doesn’t stop at the QA boundary, building expertise involves more than just testing.

Product, requirements engineering, architecture, development, security, DevOps, and leadership must also understand the changes brought about by AI.

Continuing education is particularly effective when it is linked to actual work: learning new knowledge, applying it in pilot projects, reflecting on experiences, and using them to develop new collaborative ways of working.

This is how training, consulting, and organizational development come together.

AI training at trendig

Conclusion: AI is transforming testing—and the way we develop software

By 2026, the question will no longer be whether someone has ever had ChatGPT write a test case.

AI can analyze requirements, generate test ideas, create test data, write code, investigate bugs, and handle entire task chains as an agent.

At the same time, new test objects, new risks, and new quality issues are emerging.

We must therefore learn two things:

how to test with AI—and how to test AI.

Companies must also tackle a third challenge:

integrating AI into their workflows in a way that ensures people, processes, and technology work together seamlessly.

This requires more than just tools.

It requires testing and engineering expertise, clear quality criteria, appropriate technical guidelines, transparent responsibilities, and teams that can learn together.

That’s exactly why, at trendig, we combine consulting, coaching, and training throughout the entire software lifecycle.

We help identify meaningful use cases, incorporate quality from the very beginning, test new ways of working in a controlled manner, and empower people so they can continue to develop these approaches on their own.

Because in the end, what matters isn’t how much AI is involved in your development process.

What matters is whether it results in better software.