AI is used in software testing to help draft test cases, test data, and reports, and to augment testing automation. That is different from testing software that itself uses AI: those systems still need risk-based software testing, including checks of their outputs and behavior. In both cases, AI can assist the work, but people must judge whether tests are relevant, results are accurate, and risks are covered.
Where AI fits in the software-testing workflow
AI tools can help with particular QA tasks, but generated material is a starting point—not evidence that an application works or that a test suite is complete. Applause’s 2025 survey of more than 4,400 independent software developers, QA professionals, and consumers worldwide reported these leading AI use cases among QA professionals:
| Reported use | Survey finding | What a tester should verify |
|---|---|---|
| Test case generation | 66% cited it as a top AI use case among QA professionals. | Check that each case maps to a requirement or risk, includes meaningful edge cases, and tests the intended behavior. |
| Text generation for test data | 59% cited it as a top AI use case among QA professionals. | Validate the data’s format, range, realism, privacy suitability, and compatibility with the system under test. |
| Test reporting | 58% cited it as a top AI use case among QA professionals. | Compare the report with observed results, logs, and failures; do not treat a generated summary as the test record. |
These are Applause survey findings, not universal usage rates or a controlled measure of improved speed or quality. Applause’s March 27, 2025 release quotes its EVP of High Tech & AI, Chris Sheehan: “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,” Applause’s survey release.
Generating test cases
A model can turn a user story or requirement into candidate scenarios, such as valid input, missing fields, boundary values, or a rejected transaction. The tester still needs to establish traceability to the requirement, identify omitted conditions, and confirm that the expected result is correct. A plausible-sounding test that does not exercise a real risk adds little coverage.
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Preparing test data
Text-generation tools can draft names, addresses, sample records, or other inputs. Review generated data for invalid combinations, unintended sensitive information, and whether it reflects the constraints of the application. Use approved data-handling controls: sending production data or confidential requirements to an AI service may create security or legal concerns.
Drafting reports
AI can help turn test notes into a readable summary. The report should remain anchored to what actually ran and what the system returned. Check status, environment, reproduction steps, and evidence before sharing it; a fluent summary can still omit a failure or misstate its cause.
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What adoption surveys do—and do not—show
Katalon’s State of Software Quality Report 2025 says 76% of respondents used AI-powered tools in software testing activities and 56% of QA teams still struggled to keep up with testing demands. The accessible report page does not establish either figure as a population-wide rate. Nor does it show that AI use caused the ongoing workload challenge or improved quality. Treat them as findings from that report’s respondents, not as proof of effectiveness.
Applause also reports respondent views about productivity, but the available survey evidence does not provide a controlled before-and-after estimate of how much AI changes testing speed or software quality. Teams should evaluate effects on their own work rather than infer a guaranteed gain from adoption statistics.
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Testing software that uses AI is a separate job
Using AI to help test an application is not the same as testing an AI-enabled application. When software includes an AI system or component, the test target includes its behavior and outputs as well as the surrounding software. ISO/IEC TS 42119-2:2025 provides guidance for applying established software-testing processes to AI systems and components through a risk-based approach. Its public scope describes risk identification, test approaches, and documentation, with connections to the ISO/IEC/IEEE 29119 software-testing series. The ISO/IEC TS 42119-2:2025 page summarizes the standard; access to the full text may be restricted.
Choose evaluation dimensions from the system’s risks
There is no single protocol implied for every AI product. The appropriate checks depend on its purpose, users, failure consequences, and operating context. Applause’s 2025 survey lists these human-involved AI testing activities:
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| Activity | Applause survey finding | Example evaluation question |
|---|---|---|
| Prompt and response grading | 61% cited it among top AI testing activities involving humans. | Does the response satisfy the task, and is it safe and correct for the intended context? |
| UX testing | 57% cited it among top AI testing activities involving humans. | Can intended users understand and use the AI feature, including when it fails or is uncertain? |
| Accessibility testing | 54% cited it among top AI testing activities involving humans. | Can people using assistive technologies access and operate the feature and its outputs? |
These are reported activities, not a prescription that every system requires identical tests. The percentages are Applause survey findings, not independently measured effectiveness. Human review can help assess context and usability, while repeatable checks and documented results support ongoing evaluation. Applause describes these activities in its 2025 AI Survey release.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep human judgment and test maintenance in the loop
Testers remain accountable for deciding what matters: whether cases reflect requirements, whether data is appropriate, and whether reports faithfully describe observed behavior. AI-generated artifacts need review, revision, and maintenance as requirements and software change.
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A 2025 literature review describes designing, developing, maintaining, and evolving test automation as considerable effort, and frames AI as augmentation across differing levels of automation. It does not establish that AI eliminates that work. See Ina K. Schieferdecker, “Navigating the growing field of research on AI for software testing” (June 17, 2025 preprint).
How to evaluate an AI testing tool
Assess a tool against a bounded task and your existing test process rather than assuming that a broad AI label means it fits your needs. Gartner’s February 2024 public abstract for its Market Guide for AI-Augmented Software-Testing Tools describes an evolving market and flags security and legal risks; the full vendor analysis is access-restricted. The abstract does not support a vendor ranking or detailed product comparison. See Gartner’s public abstract.
- Task fit: Identify whether you need help with test creation, data, reporting, execution, automation, or evaluating AI outputs.
- Coverage and control: Confirm you can connect generated work to requirements, risks, and edge cases, with a reviewer able to correct it.
- Integration and upkeep: Check how the tool fits current workflows and who will update generated tests as the product changes.
- Security and legal handling: Find out what data is sent, retained, or used, and whether the controls meet your requirements.
- Evidence: Separate vendor claims and survey self-reports from results measured on your own systems. Pilot against known cases and review failures as well as successes.
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Frequently Asked Questions
Does AI replace software testers?
No. It can assist specific tasks, but people still need to validate test relevance, data, results, and risk coverage.
Does a high AI-adoption percentage prove that AI improves software quality?
No. The cited figures are survey findings, not controlled evidence of quality or speed improvements.
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