Traditional testing checks software against specified behavior; testing AI-based systems also evaluates whether outputs are acceptable across relevant data, users, conditions, and risks. That does not make conventional testing obsolete: AI products still have code, interfaces, APIs, and deployments that need ordinary software checks.
“AI testing” can mean testing a system that uses AI, or using generative AI to help test other software. They are related but distinct practices. ISTQB separates testing AI-based systems (CT-AI) from applying generative AI in the testing process (CT-GenAI). ISTQB’s AI testing certifications describe that distinction.
What changes when you test an AI-based system?
In conventional software, a requirement often lets a team specify the expected result for a given input. A test can then compare actual and expected behavior. AI systems may instead produce predictions, recommendations, or generated content, and more than one output may be acceptable. Some are also non-deterministic, so identical inputs do not necessarily produce identical outputs under all conditions.
ISO/IEC TR 29119-11:2020 identifies difficulty defining acceptance criteria and deciding whether a result passes as the test-oracle problem. In practice, teams need to define an evaluation procedure or threshold rather than assume there is always one exact correct answer. The report is a published 2020 technical report, listed by ISO as under review—not the newest ISO work on AI testing. ISO’s page for ISO/IEC TR 29119-11:2020 describes its scope.
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A useful way to frame the difference is: traditional testing asks whether an implementation meets specified behavior; AI testing also asks whether system performance is acceptable across relevant data, users, conditions, and risks—and whether changes can be detected over time.
Traditional testing and AI-system testing compared
| Testing concern | Traditional software testing | Testing AI-based systems |
|---|---|---|
| Expected behavior | Requirements and rules can often define a specific expected result. | Several outputs may be acceptable. Define measurable criteria or an evaluation procedure; exact-answer assertions alone may not fit. |
| Inputs | Choose test cases to exercise requirements, code paths, boundaries, and integrations. | Test input data as well as code and system behavior. Data quality and relevance to intended use affect what a test can establish. |
| Output assessment | Exact expected values or behaviors often support direct pass/fail assertions. | Use task-appropriate metrics and judgments. For generated responses, assess against task and risk criteria rather than assume one canonical answer. |
| Repeatability | With controlled conditions, rerunning a deterministic test is generally expected to reproduce its result. | Non-determinism and changes to data or model versions require explicit repeatability and change-monitoring plans. |
| Lifecycle | Unit, integration, system, acceptance, performance, and security testing remain relevant. | Add testing of input data, models, and machine-learning development activities to the applicable software lifecycle checks. |
| Risk | Established risk-based test-management approaches address quality and security concerns. | Choose evaluation objectives and scenarios in light of intended use and potential negative impacts. |
These approaches are not mutually exclusive. ISO/IEC TS 42119-2:2025 explains how established ISO/IEC/IEEE 29119 software-testing concepts and processes apply to AI systems, with AI-specific guidance and risk-based selection of practices. ISO’s page for ISO/IEC TS 42119-2:2025 identifies the 2025 overview.
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How to adapt a testing strategy for AI
- Define acceptance criteria before choosing a score. State the task, intended users and conditions, acceptable behavior, and what counts as an unacceptable failure. The test-oracle problem is often an evaluation-design issue, not just a tooling issue.
- Include data in the test surface. Test inputs and assess whether data and scenarios represent the system’s intended use. ISTQB’s CT-AI v2.0 lifecycle includes input-data testing, model testing, and ML-development testing. See the CT-AI certification overview.
- Use evaluation lenses that match the risk. Measure task performance, and where relevant assess safety, bias, robustness, reliability, or impact. The appropriate requirements and methods vary by application; there is no single universal measure established by the guidance cited here.
- Make results interpretable over time. Record model, data, configuration, and test-set versions alongside results. Re-evaluate after material changes and consider whether input conditions or performance have shifted. ISO/IEC TS 42119-2:2025 discusses concept drift: changing statistical properties of input data can reduce model performance.
- Keep ordinary software checks. Continue applicable functional, regression, performance, and security testing for the product’s code, interfaces, APIs, integrations, permissions, and deployment configuration.
These are general recommendations, not a prescribed identical test suite for every AI application. Select methods for the system’s use and risk.
Relevant standards and guidance
ISO guidance
ISO/IEC TR 29119-11:2020 covers testing AI-based systems, including challenges such as complex, data-intensive behavior, non-determinism, and the test-oracle problem. ISO lists the November 2020 technical report as under review. ISO/IEC TS 42119-2:2025 provides an overview of testing AI systems and explains how established software-testing standards can be applied alongside risk-based AI-specific practices.
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ISTQB certification
ISTQB CT-AI v2.0 focuses on testing AI-based systems, including machine learning and generative AI. The page identifies CTFL as a prerequisite. CT-GenAI is a separate subject: using generative AI in the testing process. Certification syllabi and availability can change, so check the official page for current details.
NIST evaluation guidance
NIST’s TEVV-Athlon is an initial public draft framework for customizing test, evaluation, verification, and validation assessments to AI-system goals and context. Its scope includes statistical machine learning, large language models, multimodal models, and agentic systems. NIST’s page, updated August 14, 2026, says the public comment period closes October 6, 2026; this is draft status, not a finalized standard. Read NIST’s TEVV-Athlon page. For related technical documents, guidance, and tools, see the NIST AI Resource Center.
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What AI testing does not mean
Testing AI does not mean replacing unit tests, regression tests, security checks, or other established software-testing methods with a model score. It means adding suitable tests for data, model behavior, output evaluation, and system-specific risks while retaining the checks that apply to the rest of the product.
Nor does “AI testing” automatically mean using a chatbot to write or run tests. If generative AI assists the testing process, that is a different use of AI from evaluating an AI-based product. Keep the two meanings explicit when defining a team’s responsibilities or training needs.
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ScreenshotNeo for capturing test evidence
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Screenshot capture can help preserve visual evidence, but it does not replace evaluation of an AI system’s outputs, data, or risks.
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Use the one-call API example below to save a screenshot. Replace the URL with the page you need and supply your API key. See the ScreenshotNeo API documentation for request options.
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