Software testing tools are widely used across QA, but adoption is uneven by tool type, testing maturity, and delivery environment. In the 2024 State of Testing survey, Jira was named by 42% of respondents as a testing-related tool used to support QA, compared with 22% for Selenium and 18% for Postman. More recent SmartBear research, measured in January 2026, found that 87% of respondents had automated at least 21% of application testing while 92% still performed some testing manually.
Contents
- Testing tool adoption across QA teams
- Automation coverage and effectiveness
- AI-enabled testing tools
- What teams expect from generative AI
- Accessibility testing and organizational maturity
- DevOps priorities and quality engineering investment
Testing tool adoption across QA teams
The 2024 State of Testing results show that testing tool adoption is distributed across planning, automation, API work, and delivery management. Jira had the highest reported use among the listed tools: 42% of respondents named it as a testing-related tool supporting QA. Selenium was named by 22%, while Postman was named by 18%. Azure DevOps was named by 8%.
These figures describe the share of respondents naming each tool; they are not a market-share ranking and should not be added together. A team can use multiple tools at the same time, such as Jira for work tracking, Selenium for browser automation, Postman for API testing, and Azure DevOps for delivery workflows.
| Tool named for QA support | Respondents, 2024 |
|---|---|
| Jira | 42% |
| Selenium | 22% |
| Postman | 18% |
| Azure DevOps | 8% |
The January 2026 SmartBear study shows why tool adoption does not mean that testing has become fully automated. Although 87% of respondents had automated at least 21% of their application testing, 92% still performed some testing manually. Automation and manual testing therefore remained complementary practices in that study period.
The same study identified delivery-speed and environment-coverage pressures. Sixty percent of respondents had experienced application-quality issues during the previous 12 months because development moved faster than testing. Sixty-four percent were concerned that applications were not tested across all deployment environments. These results point to a tooling problem that is broader than selecting an automation framework: teams also need coverage across the environments in which software is released.
Automation coverage and effectiveness
In the 2024 State of Testing survey, 85% of respondents rated automation tools as effective or very effective for supporting testing tasks. Test-management tools received a similarly strong, though lower, result: 78% rated them effective or very effective.
Effectiveness was less consistent for other categories. Forty-seven percent rated load testing as not effective or not very effective for supporting their tasks. Only 25% rated monitoring tools as very effective. The measures use different response groupings, so they should be read as separate indicators rather than as directly comparable scores.
The survey results suggest that teams tend to see clear value in tools that automate repeatable testing work and organize test activity, while load testing and monitoring may present more difficult implementation or measurement challenges. The figures do not identify the causes of those differences, so they cannot establish whether the issue is tool capability, configuration, skills, coverage, or process.
SmartBear’s January 2026 findings add a forward-looking investment signal. Sixty-eight percent of respondents were concerned that faster AI-driven development would create testing and deployment bottlenecks. Sixty-five percent were concerned that their organizations under-invested in application-level testing compared with code-level testing.
Investment expectations were high. Ninety-seven percent expected their organizations to increase application-testing investment in 2026, based on a forecast made in January 2026. Eighty-six percent expected that investment to increase by at least 11%. These are respondent expectations, not measured spending outcomes, so they should be treated as forecasts rather than completed investment results.
SmartBear also reported strong expectations for autonomous application testing. Ninety-two percent said fully autonomous application testing would improve quality at least moderately, and 67% expected it to improve quality significantly or dramatically. The results indicate confidence in the potential quality impact of autonomy, but they do not show that fully autonomous testing had already been implemented or that the expected improvements had been independently measured.
AI-enabled testing tools
AI use in testing was still limited in the 2024 State of Testing survey. Sixty percent of respondents said they were not using AI tools for testing. Among the specific uses reported, 25% used AI for test-case creation, 23% for test-case optimization, and 20% for test planning.
| AI testing use or expectation | Respondents |
|---|---|
| Not using AI tools for testing | 60% |
| Using AI for test-case creation | 25% |
| Using AI for test-case optimization | 23% |
| Using AI for test planning | 20% |
The gap between current use and expected value was visible in the same 2024 survey. Fifty-one percent expected generative AI to improve test-automation efficiency. Forty-five percent expected it to create more diverse and complex test cases, while 41% expected better generation of realistic test data.
Other expected benefits were reported by smaller shares. Thirty-nine percent expected smarter bug detection. Twenty-five percent expected generative AI to reduce reliance on manual testing. Twenty-two percent expected testing roles to shift toward overseeing AI processes, and another 22% expected generative AI to help teams adapt to software changes more effectively.
These figures are 2024 forecasts from respondents, not evidence that each benefit was achieved. They also describe expectations for several different capabilities. Improving automation efficiency, generating test data, detecting bugs, and changing testing roles are distinct outcomes and should not be treated as one combined measure.
What teams expect from generative AI
The World Quality Report 2025-26 provides a later view of generative AI adoption in QA. Forty-three percent of respondents were experimenting with generative AI in QA, while 15% had scaled it enterprise-wide. The difference between experimentation and enterprise-wide scaling is important: the report describes adoption as active, but broad operational deployment remained less common among respondents.
The same report identified adoption of AI-powered tools as a test-automation challenge for 58% of respondents. This finding sits alongside the reported experimentation and scaling figures. Teams may be willing to trial generative AI while still facing difficulties with governance, integration, reliability, skills, or workflow adoption; the supplied result identifies the challenge but does not quantify its causes.
Taken together with the 2024 State of Testing findings, the data shows a transition rather than a completed shift. In 2024, 60% said they were not using AI tools for testing, while respondents still anticipated improvements in automation, test-case diversity, test data, and bug detection. In the 2025-26 report, 43% were experimenting with generative AI in QA and 15% had scaled it enterprise-wide. The survey populations and measurement periods differ, so these figures should not be interpreted as a direct year-over-year adoption rate.
Accessibility testing and organizational maturity
Accessibility testing provides a separate view of how testing tools relate to organizational capability. In FY 2024, 61% of reporting entities used at least one automated accessibility-testing tool for comprehensive, large-scale web-content monitoring. Among entities using an automated accessibility-testing tool, 84% said the personnel interpreting its results had received tool training.
Use varied substantially by maturity bracket. Automated accessibility-testing tool use was reported by 28% of entities in the very-low maturity bracket, 39% in the low bracket, 71% in the moderate bracket, 80% in the high bracket, and 93% in the very-high bracket.
| Organizational maturity bracket | Using at least one automated accessibility-testing tool |
|---|---|
| Very low | 28% |
| Low | 39% |
| Moderate | 71% |
| High | 80% |
| Very high | 93% |
The FY 2024 findings also reported tool use by conformance bracket: 42% of entities in the very-low conformance bracket, 67% in the low bracket, 64% in the moderate bracket, 82% in the high bracket, and 92% in the very-high conformance bracket used at least one automated accessibility-testing tool.
These maturity and conformance figures are descriptive group results. They show that automated accessibility-testing tool use was more common in several higher brackets, but they do not establish that tool use caused higher maturity or conformance. The 84% training result also reinforces that automated output still requires people who know how to interpret it.
DevOps priorities and quality engineering investment
The 2024 Global DevSecOps Report shows where broader technology investment priorities sat alongside testing and delivery concerns. Security and AI were each listed as a top IT investment priority by 19% of respondents. DevSecOps platforms were listed by 17%, while automation and cloud computing were each listed by 16%.
DevOps itself was a top priority for 15% of respondents. Application monitoring and observability, and continuous integration, deployment, and delivery, were each listed by 13%. MLOps and data science, and value stream management, DORA metrics, and analytics, were each listed by 12%.
| 2024 IT investment priority | Respondents |
|---|---|
| Security | 19% |
| AI | 19% |
| DevSecOps platform | 17% |
| Automation | 16% |
| Cloud computing | 16% |
| DevOps | 15% |
| Application monitoring and observability | 13% |
| Continuous integration, deployment, and delivery | 13% |
| MLOps and data science | 12% |
| Value stream management, DORA metrics, and analytics | 12% |
The pattern places testing tools inside a larger quality-engineering system. Automation is connected to application testing, while observability, continuous delivery, DevSecOps, and analytics support the conditions in which testing results are acted on. The investment figures are priority selections from the 2024 survey, not percentages of total budgets and not forecasts of realized spending.
For teams reviewing their own software testing toolchain, the statistics support several measurable questions: which testing activities are automated, how much remains manual, whether tests cover every deployment environment, how application-level testing is funded relative to code-level testing, and whether staff are trained to interpret automated results. Those questions connect tool adoption with coverage and operational capability without assuming that any single tool or AI feature guarantees software quality.
Sources
- STATE OF TESTING, 2024.
- AI Software Quality Gap Report, January 2026 and 2026 forecasts.
- World Quality Report 2025-26, 2025-26.
- Governmentwide Findings: Accessibility Conformance Testing and Technology Lifecycle, FY 2024.
- 2024 Global DevSecOps Report, 2024.