AI is used in software testing to help draft test cases, test data, and reports, and to augment test automation. A separate but related task is testing software that uses AI: teams need to evaluate the AI system’s behavior as well as the surrounding software. In both cases, AI can assist the work, but people still need to check whether tests address the right requirements, risks, and observed results.
Two different ways AI enters software testing
“AI in software testing” can refer to two distinct activities:
- Using AI to assist testing: for example, asking a tool to draft test cases, generate text for test data, prepare a report, or augment test automation.
- Testing an AI system: evaluating software that incorporates AI, including its outputs and user experience, as well as the components and conventional software around it.
The first is about using AI in the testing workflow. The second is about applying testing to software that uses AI. A team may do either or both; evidence that teams use AI tools does not by itself show that their software quality improved.
Where AI can assist the test workflow
Drafting test cases
A tester can use AI to propose cases from requirements, user stories, or a description of a feature. The draft can help surface possible scenarios, but it should be checked against the actual requirement, expected behavior, boundary conditions, and failure modes. A plausible-looking list is not proof of complete coverage.
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Generating text for test data
AI can help prepare text-based test data, such as sample inputs or content variations. Review whether the data is appropriate for the intended test, whether it covers useful edge cases, and whether it contains information that should not be sent to the tool. Avoid putting confidential or personal data into a service unless its data handling has been reviewed and approved for that use.
Preparing test reports
AI can help turn test notes or results into a draft report. A responsible reviewer checks that the report matches the recorded observations, distinguishes failures from untested areas, and does not claim a test passed when the evidence does not support that conclusion.
Augmenting test automation
AI-assisted tools may support parts of automated testing, but automation still has to fit the team’s test process and be maintained as the software changes. A 2025 literature review describes test automation as requiring considerable design, development, maintenance, and evolution effort, and considers AI augmentation across different levels of automation. Treat generated or AI-maintained tests as work to review and own, not as a maintenance-free substitute for a test suite.
What surveys say about adoption and reported uses
The figures below describe findings reported by the survey publishers. They are not universal industry rates, and they do not establish that AI caused faster testing or better software quality.
| Publisher and year | Reported finding | How to interpret it |
|---|---|---|
| Katalon, 2025 | 76% of respondents reported using AI-powered tools in software testing activities. | The accessible report page does not establish this as a population-wide rate. |
| Katalon, 2025 | 56% of QA teams reportedly still struggle to keep up with testing demands. | This is a separate reported finding; it does not show whether AI use caused or prevented the difficulty. |
| Applause, 2025 | Among QA professionals, 66% cited test case generation, 59% text generation for test data, and 58% test reporting as top AI use cases. | These are reported survey responses, not universal usage rates or measured task outcomes. |
| Applause, 2025 | More than 4,400 independent software developers, QA professionals, and consumers worldwide participated in the survey. | This describes the respondent pool; it is not a claim that the sample was random or representative. |
Applause also reported respondent beliefs about productivity, but the sources cited here do not provide a controlled, causal estimate of AI’s effect on testing speed or software quality. Survey adoption is useful context, not evidence that a particular team will get a particular result.
How to test software that uses AI
Testing an AI-enabled product includes familiar software testing and evaluation of the AI-dependent behavior. The appropriate checks depend on the product, the consequences of an incorrect result, and the risks identified for the system; the same protocol is not automatically suitable for every AI product.
Apply an established, risk-based process
ISO/IEC TS 42119-2:2025 describes applying the ISO/IEC/IEEE 29119 software testing series to AI systems and components. Its public text describes a risk-based approach covering risk identification, test approaches, and documentation, and refers to existing software-testing standards for processes, documentation, test design, and reviews. Use the standard as guidance for organizing testing; do not treat the existence of AI as a reason to discard established testing practice.
Evaluate the behavior people actually encounter
Applause’s 2025 survey lists prompt and response grading, UX testing, and accessibility testing among AI testing activities involving humans. These are useful evaluation dimensions to consider: whether outputs meet the intended criteria, whether the experience is usable, and whether people can access it. The survey reports activities, not a universal test plan or proof that any one evaluation method is sufficient.
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- Define what the AI-enabled feature is expected to do and what unacceptable behavior would look like.
- Choose test approaches in light of the identified risks and document what was tested.
- Review outputs against explicit criteria rather than accepting fluent or confident language as evidence of correctness.
- Check that reports describe observed results and limitations, including cases that remain untested.
What still needs human judgment
Across both uses, people remain accountable for deciding whether generated material is relevant and whether test evidence supports a conclusion. A tester or reviewer should check that cases map to requirements and risks, test data is suitable, and reports accurately reflect results. AI assistance does not establish that the suite covers every important case, nor does it remove the ongoing work of maintaining tests as software evolves.
Rank #4
Applause quoted Chris Sheehan, its EVP of High Tech & AI, in its March 27, 2025 survey release: “The results of our annual AI survey underscore the need to raise the bar on how we test and roll out new generative AI models and applications.” This is a company executive’s view, not an independent standard or evidence of a measured effect.
How to evaluate an AI testing tool
There is no vendor-by-vendor ranking established by the evidence described here. Evaluate a tool against your own workflow and systems rather than treating a market label or survey result as proof of fit.
- Task fit: Does it support the task you need—test-case drafting, data, reporting, automation, or evaluation of AI outputs?
- Coverage and control: Can you connect its suggestions or actions to requirements, risks, and edge cases, and review them before they become test evidence?
- Integration and maintenance: Does it fit existing test processes, and who will maintain generated or automated tests as the product changes?
- Security and legal handling: What information does the tool process, and what controls apply? Gartner’s February 2024 public abstract on AI-augmented software-testing tools describes an evolving market and flags security and legal risks; its detailed vendor analysis is access restricted.
- Evidence: Separate vendor claims and survey self-reports from results you observe on your own systems under defined conditions.
Practical use: capture a web page as test evidence
For a web application, a screenshot can be one piece of evidence in a test record—for example, a visual check of a page after a deployment. It does not replace assertions, interaction tests, accessibility evaluation, or review of the application’s behavior. If you capture pages through a screenshot API, consider whether it handles consent overlays and transient UI that could obscure the page you intend to inspect.
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