Table of Contents
opening Note
An introduction to the 13th edition of the report, marking its evolution into a Generative AI-powered analysis.
Key Findings
A high-level executive summary of the report’s four most critical discoveries.
Demographics & Background
A breakdown of the global testing workforce, covering geography, job roles, and experience levels.
Methodologies & Processes
An analysis of the tools, structures, and metrics defining modern QA.
AI & Automation Adoption
A deep dive into the 76.8% adoption rate of AI in testing.
Personal Perspective
An examination of the human side of the profession.
Opening Note
Welcome to the 13th edition of the State of Testing™ Report.
Since 2013, we have served as the global benchmark for quality engineering. This year, the report itself evolves. We are proud to introduce our first edition, powered by Generative AI analysis, paired with a dynamic new design to match the modernization of our craft.
Our deep-dive analysis reveals an industry in flux: a workforce navigating the “AI Paradox,” a sharp divergence in “Modern” vs. “Legacy” salaries, and a profession redefining its value in real-time.
Thank you to the thousands of practitioners who contributed to our most advanced dataset yet. We invite you to explore these insights to see not just where we stand today, but where we are headed tomorrow.
Joel and Lalit
Key Findings
1. The "AI Paradox": Anxiety in the Absence of Action
AI has firmly established itself as the singular dominant force in the industry, with 78.8% of professionals citing it as the most impactful trend for the next five years—dwarfing DevOps and Shift-Left combined. However, this consensus has birthed a “Panic Majority,” where 65.6% of the workforce reports being “Very Concerned” about the future of their profession.
- The Critical Insight: Anxiety is inversely correlated with usage. Professionals who actively use AI are 17% less anxious and 4x more likely to have “Zero Concern” than their non-adopting peers. The data suggests that for testing teams, the cure for existential fear is not reassurance, but hands-on application.
2. The "Specialist Penalty": The Ceiling on Technical Skills
Our salary analysis uncovers a brutal reality for senior professionals (10+ years experience): sticking purely to a technical track effectively caps earning potential. While technical skills are the price of entry for junior roles, they yield diminishing returns at the senior level.
- The Data: Senior professionals who prioritize “Leadership & Strategy” skills earn a +10.6% income premium, while those relying solely on “Technical Execution” skills (like automation scripting) face a -13.8% income penalty. The market message is clear: To maximize value after a decade in the field, one must pivot from executing code to influencing strategy.
Key Findings
3. The "Strategic Personality" of Sectors
Not all industries are adapting to the new era equally; they are evolving distinct survival strategies based on their unique risks:
- The AI Hyper-Adopters: The Transportation sector leads the world in AI conviction (89%), driven by the existential necessity of autonomous systems.
- The Risk-Averse: Finance has successfully pivoted to prevention, leading the industry in Shift-Left Testing (31.7%) to mitigate regulatory risk.
- The Blind Spot: Retail appears to be trading safety for speed, reporting the industry’s lowest focus on Security & Compliance (21.4%)—a dangerous outlier in an era of increasing data privacy demands.
4. The Systemic Trap: The "Faster Horse" Phenomenon
A cross-analysis of how teams are measured and how they apply AI reveals a critical flaw in the industry’s current trajectory: we are using 21st-century tools to optimize 20th-century objectives.
- The Measurement Trap: Organizations overwhelmingly evaluate teams on volume rather than value. While 56% of teams are measured on Test Coverage (“Did we run everything?”), only 4.5% are measured on NPS (“Did the customer care?”).
- The AI Trap: This “volume-first” mindset has infected AI adoption. Rather than using AI to escape the “test factory”, teams are simply building a faster factory. 70% use AI for Test Case Creation (generating more scripts), while only 19.9% use it for Risk Identification (strategic decision-making).
- The Conclusion: The industry is currently stuck in a “Faster Horse” cycle—producing more tests than ever before, but not getting any closer to the strategic goal of acting as true business enablers.
Where are you from?
My company's HQ is in a different region from where I live
5.5%
What is your current job title?
How many years of experience do you have in testing?
What is your company's headcount?
What industry do you work in?
What is your average annual income in USD from testing and testing-related activities?
| Region | Less than 1 year | 1–2 years | 3–5 years | 6–10 years | 10+ years |
|---|---|---|---|---|---|
| Asia | $6,000 | $11,500 | $15,500 | $24,500 | $43,000 |
| Western Europe | $12,500 | 27,000 | $41,500 | $60,500 | $88,000 |
| North America | N/A | $20,000 | $55,000 | $89,000 | $132,000 |
| Eastern Europe | N/A | $12,500 | $29,000 | $41,500 | $61,000 |
| Africa | N/A | $8,500 | $28,000 | $38,000 | $44,500 |
| Latin America | N/A | $15,556 | $32,500 | $37,000 | $63,846 |
| HQ differs from region | $5,000 | N/A | $18,500 | $49,500 | $78,500 |
Salary figures are displayed only for sufficient data to ensure statistical accuracy.
The "Sweet Spot" for Earnings is in Large, but Not Massive, Companies
| Company Headcount | 1–2 Years | 3–5 Years | 6–10 Years | 10+ Years |
|---|---|---|---|---|
| 1–10 | $7,500 | $15,000 | $48,750 | $79,500 |
| 11–50 | $6,250 | $27,037 | $22,000 | $47,895 |
| 51–200 | $8,462 | $20,857 | $42,188 | $76,324 |
| 201–500 | $8,500 | $25,385 | $44,211 | $79,762 |
| 501–1,000 | $9,266 | $17,667 | $36,429 | $65,682 |
| 1,001–5,000 | $11,000 | $28,572 | $60,834 | $65,834 |
| 5,001–10,000 | $25,834 | $38,572 | $76,334 | |
| 10,000+ | $19,167 | $29,167 | $73,463 |
For testing professionals with up to 10 years of experience, the most lucrative career path is consistently found within
large organizations (1,001–5,000 employees). This group offers significant financial advantages, paying
26% above the industry baseline for entry-level roles and a remarkable
52% above the baseline for mid-senior professionals (6–10 years).
In stark contrast, companies with 11–50 employees represent the most financially challenging environments.
For professionals with 6–10 years of experience, the gap is most pronounced: those in large organizations earn
176% more—nearly triple the average salary—than their counterparts in these smaller firms
($60,834 vs. $22,000). While large companies remain competitive for senior leadership (10+ years),
they eventually trail the industry baseline by 6%, as mid-sized companies and funded startups
begin to offer higher premiums for top-level expertise.
Which testing-related tools do you use?
Average Annual Income by Tool Skill
Tooling governance has emerged as a critical “Maturity Index” in this year’s data, revealing that the adoption of dedicated infrastructure is a powerful proxy for organizational sophistication rather than a mere administrative preference. We observe a distinct “Professionalism Premium,” where the structure provided by Test Management tools correlates with a 23.7% salary increase and enables significantly higher AI adoption rates (83.1%) by providing the clean, structured data necessary for innovation.
This economic divergence extends sharply into the automation landscape, creating a definitive career fork between legacy stability and modern earning potential: while Selenium remains the entrenched standard within large enterprises, Playwright has established itself as the lucrative disruptor, commanding a massive 38% salary premium over its predecessor. Ultimately, the market is signaling a clear trade-off between the corporate security of established ecosystems and the maximized financial value of modern frameworks.
Average Annual Income by Tool Skill
What percentage of your testing is outsourced or handled by external vendors?
The “Corporate Dip” in Outsourcing
Strategic Sourcing: The “U-Curve” of Reliance
Our data reveals that outsourcing is not a linear strategy, but a
“U-shaped” curve tied to organizational scale:
- The Mega-Corp Shift: Size forces dependency. Massive organizations (10k+ employees) are the heaviest outsourcers (30.7% average), with 1 in 5 firms outsourcing the majority of their work (>70%) to shift internal focus from execution to governance.
- The “Fortress” Anomaly: The sweet spot for independence is the 5k–10k employee tier. These firms report the industry’s lowest outsourcing rates (11.7%), possessing the scale for world-class internal labs without the bloat that necessitates vendors.
- The Startup Binary: Early-stage companies face an “All or Nothing” choice: they either keep testing 100% in-house (to protect speed/IP) or outsource everything (to survive cash burn).
The “Corporate Dip” in Outsourcing
At what stage of the SDLC are testers most involved in your organization?
- The “Massive Corporate” Shift Left: Companies with 10,000+ employees have the highest involvement in Early Coding / Unit Testing (42.3%). This is significantly higher than any other group, suggesting that in massive organizations, “Shift Left” is not just a buzzword but a structural reality where QA is deeply integrated into the development phase. They also dominate Integration / System Testing (77.5%), reflecting the complexity of their ecosystems.
- The “Fortress” Anomaly (Again): The 5,001–10,000 group, which we previously identified as the most self-sufficient and least outsourcing-reliant, shows a very conservative testing approach here. They have the lowest involvement in Unit Testing (16.7%) and Post-Production (16.7%), preferring to focus their energy on the traditional middle stages (Requirements, Integration, Pre-release).
Which metrics is your team evaluated by?
- The Reality Gap: Despite the industry’s push toward “Quality Engineering” and business alignment, our data reveals that most organizations still measure their QA teams based on volume rather than value.
- Activity Over Outcomes: The results show that in the vast majority of cases, QA and testing professionals are measured on activity-based metrics. The dominant KPIs remain Test Coverage (56.4%) and Automation Coverage (40.1%)—metrics that track how much work is being done, but not necessarily how effective that work is at protecting the user experience.
- The Missing Business Link: In stark contrast, true outcome-related metrics are rarely used to evaluate testing performance. A mere 8.6% of teams are evaluated based on Business Impact.
- The Road Ahead: This disparity indicates that there is still a significant road to travel in evolving the perception of QA. For most organizations, the testing function is still viewed as a tactical execution team (“Did you run the tests?”) rather than a strategic business enabler (“Did we release a high-quality product that delights users?”). Shifting this measurement model is the next critical frontier for testing leadership.
Where is QA located in the organizational chart?
Our analysis confirms that where you sit determines what you earn and how early you test.
- The Salary Gap: Proximity to code equals proximity to value. Testers working in cross-functional squads earn ~$46,250—a 27% premium (+$10k) over peers in traditional siloed QA departments ($36,500).
- The “Shift-Left” Enabler: Structure dictates timing. 56.5% of embedded teams are involved in the Requirements Phase, compared to 51.5% of standalone units. The data suggests that if you want to Shift Left, you must first Shift Structure.
- The Scale Reversion: The “Fortress” tier (5k–10k employees) represents the Agile peak, boasting the industry’s highest embedding rate (63.3%). However, at 10k+ employees, gravity takes over: embedding drops to 39.4% as massive organizations revert to centralized “Chapters” to regain governance over their sprawling ecosystems.
- The Industry Flip: Finance defies stereotypes, leading with 56.1% embedded squads. Conversely, Retail remains the most traditional (25% embedded), opting for centralized efficiency over agile speed.
Compared to the previous year, were there any changes in:
- The Healthcare “Squeeze”: This sector faces the most alarming operational gap. While 65.4% of teams report increased workloads, they also report the lowest budget growth (11.5%) and the highest rate of team downsizing (53.8%), indicating a critical resource shortage.
- Finance Volatility: The Finance sector is aggressively transforming, showing the highest workload increase (68.3%) and highest automation adoption (67.1%). However, it also faces high budget cuts, reflecting a polarized market.
- Transportation & Retail Stability: These sectors are the “calm zones”, with nearly half of respondents reporting no change in resources. However, Retail lags significantly in automation, potentially signaling a future competitive disadvantage.
| Industry | Workload | QA Budget | Team Size | Automation |
|---|---|---|---|---|
| Finance / Insurance | 68.3% | 26.8% | 37.8% | 67.1% |
| Health Services | 65.4% | 11.5% | 11.5% | 50.0% |
| Internet / Tech | 63.6% | 22.4% | 28.5% | 54.4% |
| Retail | 60.7% | 14.3% | 32.1% | 35.7% |
| Transportation | 48.1% | 22.2% | 22.2% | 51.9% |
Do you currently use AI in your organization?
| Adoption Rate | |
|---|---|
| Global Average | 76.8% |
| Decreased Workload | 85.7% (+11.6%) |
| Enterprise (10k+ employees) | 81.7% (+6.3%) |
| Small Business (1–10 employees) | 70.6% (−8.1%) |
The “AI Pay Raise” is Real
There is a direct financial correlation with AI adoption. Professionals who report using AI in their organizations earn significantly more than those who do not.
AI Users Average Income ~$45,400 vs. Non-Users Average Income ~$35,800
This represents a ~27% salary gap, suggesting that AI literacy is now treated as a high-value skill in the market, distinguishing “modern” testers from traditional ones. Alternative reasoning could be related to the types of organizations that adopt AI.
Scale Drives Adoption
Large enterprises, likely driven by the need to manage massive complexity and scale, are adopting AI faster than agile startups. This contradicts the common perception that large corporations are “slow movers”—in the case of AI, they are the power users.
10,000+ Employees: 81.7% adoption (Highest) vs. 1–50 Employees: ~71% adoption (Lowest)
Where are AI tools being used in your testing process?
Which AI tools are currently being used in your QA process?
Similar to the findings from the performance measurement question, the industry once again overwhelmingly treats AI as “Extra Hands” for execution (Creation 69.6%, Maintenance 59.6%) rather than
“Extra Brains” for strategy. However, the specific role depends on company size.
Large firms use AI to scrub legacy debt. 65.5% use it for script maintenance (vs. 41.7% of startups), keeping old regression suites alive.
Small teams use AI to fill the leadership gap. They significantly outperform giants in
strategic tasks like Test Planning (50.0% vs. 31.0%) and Risk ID (33.3% vs. 12.1%).
Which AI tools are currently being used in your QA process?
What do you see as the main benefits of AI adoption in testing?
There is a fascinating divergence between what Non-users think AI will do versus what users actually experience.
The Myth of Manual Reduction: Non-adopters heavily overestimate AI’s ability to replace manual work, when 44.1% expect “Reduced reliance on manual testing”, but only 30.9% of adopters report this as a main benefit.
The Reality of Complexity: In contrast, actual users find AI’s true value in handling complexity. 40.7% of adopters cite “More diverse and complex test cases” as a key benefit, compared to only 28.8% of non-users.
Experience teaches that AI doesn’t just “do the manual work for you”—it empowers you to cover more complex scenarios that were previously impossible to test.
What are the main barriers to adopting AI in your QA team?
When we zoom in, distinct “Pain Profiles”emerge. Companies aren’t just blocked by AI; they are blocked by their specific business reality.
- Finance is Blocked by Compliance: For Finance/Insurance, the barrier is regulatory. 54.9% cite security as their primary hurdle—15 points higher than in Retail. They have the money and the talent, but they are handcuffed by red tape.
- Retail is Blocked by Budget: The Retail sector is the “Cost Skeptic.” It has the lowest concern for skills (21.4%) but the greatest concern for ROI (35.7%). Operating on thin margins, these teams are blocked not by difficulty but by the business case.
- Healthcare is Blocked by Complexity: Health Services struggles with the Complexity of AI tools (38.5%). Likely due to strict validation requirements (e.g., FDA), integrating “black box” AI into life-critical workflows creates a validation burden other industries don’t face.
- Transportation is Blocked by Uncertainty: The Transportation sector has the highest rate of Uncertainty about Benefits (44.4%). This suggests a lack of clear, proven use cases for AI in logistics/transport testing compared to digital-native sectors.
How would you define the maturity level of the following practices in your organization?
| AI | 34.9% | 50.6% | 12.4% | 2.1% |
|---|---|---|---|---|
| Automation | 16.4% | 31.2% | 38.2% | 14.3% |
| Exploratory Testing | 10.4% | 28.1% | 44.2% | 17.3% |
| Risk-Based Testing | 17.6% | 33.4% | 36.8% | 12.2% |
| Monitoring/ Testing in Production | 14.5% | 25.3% | 37.7% | 22.5% |
The Lucrative Correlation: Maturity pays. Testers in organizations with “Optimized” practices earn an average of $57,167—a massive 62% premium over peers in “Initial/Experimenting” environments
($35,129).
This gap proves that sophisticated processes require sophisticated talent. Mature organizations are willing to pay a premium to maintain their “Quality” status, creating a lucrative career path for those who can drive process improvement.
Comparing giants to startups reveals that necessity drives proficiency:
- Scale demands oversight: Large enterprises (10k+) significantly outperform startups in Production Monitoring (25.4% vs. 11.8%) and Risk-Based Testing (+10.9 point gap). Complex systems force them to excel at prioritizing risks and watching production.
- The Startup “Human” Edge: The one area where small teams beat the giants is Exploratory Testing (20.6% vs. 16.9%). Without the safety net of massive regression suites, startups rely on skilled human exploration as their primary defense, cultivating a proficiency born of pure necessity.
What skills and knowledge areas do testers need to thrive?
Our analysis reveals a fundamental shift in market value as professionals advance in their careers. While technical proficiency is the point of entry for junior roles, it ceases to be a driver of financial growth at the senior level. In fact, sticking to a purely technical path as a senior professional correlates with a statistically significant “specialist penalty.”
| Skill Category | Skill | Relative Salary Premium |
|---|---|---|
| Leadership & Strategy | Communication Skills | +36.1% (Highest Multiplier) |
| Leadership & Strategy | Critical Thinking | +7.7% |
| Technical Execution | API Testing | -5.7% |
| Technical Execution | Programming Skills | -0.1% (Neutral) |
The Leadership Premium vs. The Technical Tax When aggregating skills into broad categories for Senior Testers (10+ years of experience), a stark contrast emerges:
- Leadership & Strategy skills (Communication, Critical Thinking, etc.) deliver an average income boost of +10.6%.
- Technical Execution skills (Automation, API Testing, etc.) correlate with an average income drop of -13.8%.
The Strategic Implication: This data suggests that the “Leadership Premium” is a systemic reality. As professionals climb the career ladder, the market stops paying for what they can execute. and starts paying for what they can drive with their mind, i.e., strategy. Senior professionals who continue to define themselves primarily by their hands-on capabilities effectively cap their earning potential compared to those who pivot toward influence, strategy, and communication.
Which skills do you feel are most underdeveloped in your QA team today?
Nearly 40% of Individual Contributors feel that “Test Strategy” is missing or underdeveloped. They feel directionless, while only 25% of Leaders agree.
Leaders believe they are providing clear direction, but the message is not landing. Leaders believe the destination is set, but the teams on the ground feel they are operating without a compass
How concerned are you about the long-term future of QA as a profession?
The most striking finding is the sheer level of anxiety among those doing the work. The further you are from the code, the safer you feel.
68.9% of Practitioners (ICs) are “Very Concerned.” They are the most exposed group. On the other hand, only 55.6% of Strategy Leaders are “Very Concerned.” They are significantly more insulated.
Leaders are 19% less anxious about the future than the teams they lead. Leaders likely see the strategic evolution, while practitioners feel the immediate threat of replacement.
What do you believe will have the biggest impact on software testing in the next 5 years?
What do you believe will have the biggest impact on software testing in the next 5 years?
The Path Forward: Turn Anxiety Into Action
Test Management users earn
23.7%
more
The Path Forward: Turn Anxiety Into Action
The 2026 data points to an “Anxiety Gap”: concern is real, but it is highest where AI is least used. The way out is not running more tests. It is running a better quality operation.
We found a clear “Professionalism Premium”: professionals who use dedicated test management tools earn 23.7% more than their peers and are 13.5% more likely to adopt AI successfully. In other words, structure reduces uncertainty and makes AI adoption practical.
The industry is moving away from ad-hoc execution and toward structured, strategic quality engineering. The shift is simple: from running tests to managing quality.
That shift is at the core of PractiTest. We help teams build the structure, visibility, and AI readiness they need to modernize testing and make confident decisions as the pace of change accelerates.
The Multilingual State of Testing
- Chinese 下载中文版报告: 2020 I 2019 I 2018 I 2017. Many thanks to Akui Yu, Licai Jin, and their team.
- Japanese ダウンロード: 2021 I 2020 I 2019 I 2018 I 2017 I 2016 I 2015 I 2013. Many thanks to Keizo Tatsumi, Noriyuki Nemoto, Yusuke Nakamura, and Yoshiwo Yano.
- Vietnamese Tải về Bản báo cáo bằng Tiếng Việt: 2022 I 2021 I 2020. Many thanks to Tu Ngo and the QA department at PYCOGROUP.
Have questions or want to help translate the next edition?
About PractiTest
PractiTest is an enterprise-grade, end-to-end test management platform that centralizes your QA processes, unifying teams and tools into one streamlined hub. Designed for efficiency, PractiTest combines robust testing capabilities with a user-friendly experience and an AI-driven trusted companion that delivers personalized insights to guide and optimize your testing in real time.
Customizable dashboards and real-time reporting provide full visibility and control, while seamless integrations with multiple ticketing, automation, and CI/CD tools ensure cohesive workflows. PractiTest empowers QA teams to make data-driven decisions, reduce effort, and deliver higher-quality products faster and more efficiently.
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