Software quality assurance is changing as teams adopt AI, expand automation, and connect testing more closely with platform engineering. Recent industry reports show strong interest in AI-assisted quality work, but also persistent gaps in test coverage, automation strategy, documentation, and delivery controls. The figures below are attributed to the named reports and their stated survey or measurement periods.
Contents
- AI adoption and trust
- Quality engineering and platform teams
- Automation maturity and testing effectiveness
- The leading QA challenges
- Testing goals and delivery performance
- AI use cases in software testing
AI adoption and trust
The DORA 2024 infographic indicates that AI is already part of everyday software work for many respondents. It reports that 76% rely on AI for tasks such as code writing, information summarization, and code explanation. At the organizational level, 81% said their company had shifted resources into developing AI, while 67% said AI was helping them improve their code.
Adoption is not the same as confidence. In the same DORA 2024 infographic, 39% reported little or no trust in AI. This creates a quality assurance tension: teams may use AI broadly while still requiring human review, traceability, and safeguards before generated code or test assets are accepted.
DORA also associated a 25% increase in AI adoption with several workflow changes. The reported associations included a 7.5% increase in documentation quality, a 3.4% increase in code quality, and a 3.1% increase in code review speed. Approval speed was associated with a 1.3% increase, while code complexity was associated with a 1.8% decrease. These are reported associations, not guarantees that adoption alone caused each result.
The DORA 2023 report connected broader organizational practices with performance. Generative cultures were linked to 30% higher organizational performance, and user-focused teams were linked to 40% higher organizational performance. High-quality documentation was linked to 25% higher team performance. Public cloud was linked to a 22% increase in infrastructure flexibility, while flexible infrastructure was linked to 30% higher organizational performance.
The same DORA 2023 report also recorded workforce differences that matter for sustainable QA operations. Underrepresented respondents reported 24% more burnout and 29% more repetitive work. These figures suggest that quality programs need to consider not only tooling and release speed, but also how repetitive verification work and operational pressure are distributed across teams.
Quality engineering and platform teams
Capgemini’s World Quality Report 2024 shows that platform engineering and quality engineering are increasingly connected. Forty-three percent of respondents said their platform had a dedicated security and compliance team. In addition, 51% said platform teams were responsible for enforcing software and tool versions for security updates. The report also recorded an average of three self-service platforms internally.
Workflow automation is a major platform responsibility: 66% said automating workflows and processes was in scope for their platform engineering teams. Product ownership also appears important, with 52% saying a product manager is crucial to platform team success. Sixty-five percent said the platform team was important and would receive continued investment.
Gen AI adoption in quality engineering was still uneven in the Capgemini 2024 report. Thirty-four percent of organizations were actively using Gen AI in quality engineering, and another 34% had Gen AI roadmaps after successful pilot implementations. Among respondents reporting Gen AI integration, 72% said automation processes were faster.
Learning programs were common but not always measured consistently. Eighty-two percent of organizations reported dedicated learning pathways for quality engineering teams, while 50% actively tracked the effectiveness of those programs. This distinction matters because providing training and measuring its effect are separate management activities.
Sustainability was also measured in the Capgemini 2024 findings. Twenty-five percent of organizations measured the environmental impact of their overall IT development, and 44% tracked the impact of testing activities on the environment. Thirty-four percent of respondents said they were implementing efficient quality engineering practices to drive sustainability.
Automation maturity and testing effectiveness
Katalon’s State of Software Quality 2024 included more than 3,800 quality engineers and more than 10 industry expert interviews. Among the testing approaches measured, 69% rated automated integration or system testing as effective. Automated unit testing and behavior-driven development each received a 66% effectiveness rating.
TestRail’s Software Testing & Quality Report 2024 presents a different view of automation maturity. Forty percent of respondents said they were automating their tests. The desired future automation level was 56%, producing a 16-percentage-point gap between current and desired levels. This gap indicates that many teams view automation as strategically valuable while still operating below their intended coverage or capacity.
Deployment frequency was relatively high among the TestRail 2024 respondents: 69% used monthly or more frequent deployments. Frequent delivery increases the importance of dependable regression coverage, fast feedback, and clear release criteria because testing must keep pace with the flow of changes.
JetBrains Qodana’s State of Software Quality 2024 reported that 27% of tech leaders lacked automated quality gates for every merge request. The absence of a quality gate at every merge point can leave inconsistent checks between branches or teams. The same report identified manual testing as the most resource-intensive quality practice for 39% of tech leaders.
Applause’s 2024 State of Digital Quality report used 748,000 test runs as its representative sample. It covered 70 industries and 156 countries. Those scope figures describe the breadth of the report’s evidence base and show that digital quality measurement can span diverse products, markets, and testing contexts.
The leading QA challenges
Katalon’s 2024 survey identified time pressure as the most frequently cited challenge in this dataset: 48% cited a lack of time to ensure quality. Four challenges each received a 34% share: applying test automation, frequent changes in requirements, and a lack of experienced and skilled resources. A further 24% cited a lack of mature tools or technology.
Capgemini’s World Quality Report 2024 highlighted structural barriers. Fifty-seven percent identified a lack of comprehensive test automation strategies as a barrier, while 64% identified reliance on legacy systems. These barriers can reinforce one another: older systems may be harder to automate, while incomplete strategy can make modernization efforts difficult to prioritize.
JetBrains Qodana’s 2024 report found that 52% of tech leaders faced challenges caused by conflicting priorities and usability issues. This points to a coordination problem as well as a tooling problem. Even when teams have testing platforms available, quality work can compete with delivery deadlines, maintenance, security, and product changes.
TestRail’s Software Testing & Quality Report 2025 identified several major challenges. Thirty-three percent cited end-to-end testing across integrated systems, 32% cited developing automated tests, and 32% cited being involved too late in the development cycle. In the same report, 58% said rapid releases lead to defects slipping into production.
The 2025 TestRail figures connect process timing with escaped defects. Involving QA earlier may help teams address risk before integration and release pressure peak, while stronger automated testing can support the faster release cadence reported by many organizations. The figures do not quantify the effect of any particular intervention, so they are best read as indicators of where respondents experience friction.
Testing goals and delivery performance
Katalon’s State of Software Quality 2024 reported that 47% aimed to integrate AI into QA processes as their main QA objective. Among managers and senior management, the share selecting AI integration into QA processes as the key goal in coming years was 58%. This difference shows stronger AI prioritization among leadership respondents than in the overall objective measure.
TestRail’s Software Testing & Quality Report 2025 placed test coverage at the top of the listed team goals. Thirty-five percent ranked increasing test coverage first, 20% prioritized reducing bugs in production, and 13% focused on automating more tests. Coverage therefore led the measured priorities, while defect reduction and automation remained distinct objectives.
For delivery performance, the DORA 2023 infographic showed a 5% change failure rate for top performers. It also listed a deployment frequency benchmark of less than one day and a failed-deployment recovery time benchmark of less than one hour for top performers. These are benchmarks for the top-performer group in that infographic, not universal targets for every software team.
The relationship between speed and quality is visible in the TestRail 2025 finding that 58% associated rapid releases with defects slipping into production. DORA’s top-performer benchmarks indicate that high delivery speed can coexist with controlled failure and recovery measures, but the reports use different samples and definitions. The figures should therefore be compared as separate indicators rather than combined into a single performance score.
AI use cases in software testing
TestRail’s Software Testing & Quality Report 2024 found that 54% of respondents did not currently integrate AI into QA efforts. Among the reported uses, 22% used AI to craft test cases or scenarios, 19% used it to generate test automation scripts, 14% used it for managing test data, and 12% used it for debugging test code.
A separate TestRail AI in QA report from 2024 reported that 65% already leveraged AI in QA processes, while 35% had not yet adopted AI in QA. Because these figures come from a separately named report, they should not be treated as a direct time-series comparison with the Software Testing & Quality Report 2024.
TestRail’s 2025 AI in QA article reported that 54% of QA professionals were using ChatGPT and 23% were using GitHub Copilot. Katalon’s State of Software Quality 2024 reported that 52% expected AI-generated test cases for manual testing to be the most anticipated application. The same anticipated-use figure was reported separately as 52% of Katalon participants, so it is counted here once.
| AI or quality measure | Reported figure | Source and period |
|---|---|---|
| Reliance on AI for code-related tasks | 76% | DORA 2024 infographic |
| Companies shifting resources into AI | 81% | DORA 2024 infographic |
| Organizations actively using Gen AI in QE | 34% | Capgemini World Quality Report 2024 |
| Respondents using AI to craft test cases | 22% | TestRail Software Testing & Quality Report 2024 |
| QA professionals using ChatGPT | 54% | TestRail AI in QA report 2025 |
| Teams expecting AI-generated manual test cases | 52% | Katalon State of Software Quality 2024 |
Taken together, the statistics show a field moving from experimentation toward operational integration. AI adoption is widespread in general software work, while quality engineering adoption is lower in the Capgemini measure and use-case adoption varies across TestRail measures. The continuing gaps in automation strategy, quality gates, test coverage, and skilled resources explain why AI interest has not removed the need for disciplined software testing practices.