AI can help software testers generate and analyze tests, but that is different from testing software that contains AI. In the first case, AI is a tool in the testing process; in the second, the AI system itself—including its data and model behavior—must be tested. In both, human judgment remains essential: generated results need review, and AI-based products require expectations that account for probabilistic, data-dependent behavior.
What does “AI in software testing” mean?
The phrase covers two related but distinct activities. Conflating them can leave a team with tests for the wrong risks: a generative AI tool assisting a tester is not the same test subject as a product whose own behavior is generated by a model.
| Activity | What is being tested? | What AI contributes |
|---|---|---|
| Using AI to test conventional software | A conventional application, service, or feature | A tool may help create tests, analyze code or failures, prioritize execution, or maintain automation. |
| Testing AI-based software | A product that relies on AI or machine learning | AI is part of the system under test. The test strategy must cover its data, model behavior, and development lifecycle. |
ISTQB reflects this distinction with separate certification tracks: CT-GenAI focuses on applying generative AI in the test process, while CT-AI v2.0 focuses on testing AI-based systems. These are different learning goals, not alternate names for one discipline.
How is AI used in software testing?
AI-assisted testing describes possible ways to support test work, not a guarantee that a tool will improve quality or save time in every project. A 2025 secondary mapping study by Katja Karhu, Jussi Kasurinen, and Kari Smolander groups reported or proposed uses across several parts of the testing workflow:
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- Planning and design: analyze requirements, suggest test cases, or generate test scripts for a tester to review.
- Implementation and execution: assist with UI testing or intelligent test automation, and help execute or maintain tests.
- Analysis and prioritization: examine code or failures, support root-cause analysis, prioritize tests, or predict defects.
These categories describe application areas in the mapped literature; they do not establish that every use is mature, widely adopted, or beneficial in every setting. A suggested test can miss an important requirement, encode a mistaken assumption, or fail to reflect the actual system. Treat AI output as a candidate for validation against requirements and observed behavior, not as evidence that a requirement has been met.
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For conventional web applications, screenshots can help a team inspect a rendered page or compare visual states. ScreenshotNeo is a website screenshot API—not an AI testing system—and can capture a page as an image or PDF. It can provide visual evidence for a test workflow, but it does not decide whether a result is correct or replace review by a tester. Learn more at ScreenshotNeo.
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How do you test software that contains AI?
AI-based systems can be probabilistic, non-deterministic, and dependent on data. As a result, a test strategy cannot assume that every input will always produce one exact, repeatable output. ISTQB’s CT-AI v2.0 outline organizes coverage around input data testing, model testing, and machine-learning development testing, and includes generative AI and large language models.
Test input data
Data is part of the behavior being tested, not merely setup material. Check whether the input data is suitable for the intended task and whether the system’s acceptance criteria account for the data it will receive. Where data characteristics affect results, test those characteristics explicitly instead of treating a successful run on one example as proof of general behavior.
Test model behavior and performance
Define what an acceptable result means for the product and evaluate behavior against those criteria. The CT-AI outline includes AI/ML quality characteristics, acceptance criteria, functional performance metrics, and neural networks. For generative AI and LLM features, tests should address the outputs the product is meant to produce as well as the ways those outputs can fail. Exact wording may vary between runs, so an acceptance approach based only on matching one fixed response may not be appropriate.
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Test the ML development lifecycle
Include the development process that produces the model in the test scope. The CT-AI framework treats ML development testing as a distinct coverage area alongside data and model testing. This helps teams avoid focusing exclusively on the visible interface while overlooking the data and model work on which the product depends.
These areas are a structure for coverage, not a universal test recipe. The product’s purpose, risks, data, and acceptance criteria determine which checks are needed and what evidence is sufficient.
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What should human testers check when AI generates test cases?
ISTQB’s CT-GenAI material explicitly includes evaluating generated results and managing hallucinations, reasoning errors, bias, privacy, and security risks. A practical workflow, inferred from that emphasis, is to keep people responsible for decisions that require product context or carry release risk:
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- Confirm the requirement: check that each proposed test traces to a real requirement or risk rather than an invented or outdated assumption.
- Review test quality: inspect steps, inputs, expected results, and edge cases before using generated tests as coverage.
- Validate explanations and analysis: independently check claims about failures, likely causes, or code behavior against the system and available evidence.
- Apply privacy and security rules: decide whether it is permissible to send the relevant code, test data, or other information to a particular AI tool.
- Set priorities and release criteria: choose which risks warrant testing and decide whether the results support release.
This is practical guidance, not a measured universal allocation of work. The sources do not establish that every team should divide testing tasks in the same way, or that human review alone makes an AI-generated result reliable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Will AI replace software testers?
The available evidence does not support a simple replacement claim. AI can assist with parts of test design, automation, analysis, or maintenance, but those possibilities do not remove the need to understand requirements, product context, risk, and evidence. An AI-generated test or explanation still needs to be judged for relevance and correctness.
The AI-T ontology paper, published in 2020, describes a conceptual framework intended to support human testers, guide intelligent agents in generating or reusing test cases, help agents learn about testing, and aid mixed human–agent teams. That establishes collaboration as a design possibility; it does not prove that a specific agent or workflow performs well.
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What does the industry evidence show—and not show?
Karhu, Kasurinen, and Smolander’s study, dated April 7, 2025, mapped industry-context research from 2020 onward. The authors report that AI was not yet heavily utilized in software testing in the evidence they mapped, and that industry-context studies and observed benefits were limited. The study distinguishes potential use cases from actual implementations; it is not a controlled estimate of how much AI improves test speed or quality.
The paper also cites Perforce survey results. Read them as findings about survey respondents, not as measurements of all software organizations or proof of causal impact:
| Reported result | Attribution and qualification |
|---|---|
| 48% interested in AI but had not started initiatives; 11% already implementing AI techniques in software testing | Perforce, 2024, as cited in the 2025 secondary study. |
| Over 75% identified AI-driven testing as pivotal to their 2025 strategy; 16% reported adopting AI in testing | Perforce, 2025, as cited in the 2025 secondary study. |
The figures describe different survey questions and years; they should not be read as a single trend line or as evidence that adoption produced better outcomes. The mapped evidence does not establish a broadly generalizable causal estimate for how much a human–AI workflow improves quality or speed.
Which ISTQB learning path fits your work?
Choose based on which of the two testing problems you need to address. The official ISTQB descriptions list the Certified Tester Foundation Level (CTFL) as a prerequisite for both paths.
| Path | Best aligned with | Study routes described by ISTQB |
|---|---|---|
| CT-AI v2.0 | Testing AI-based systems, including data, models, and ML development | Syllabus, sample exam, and training-provider routes. |
| CT-GenAI | Using generative AI in software testing and evaluating its results and risks | Accredited training and self-study. |
Exam arrangements and provider availability can vary or change; check the current ISTQB information for your region before choosing a course or booking an exam.
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