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Software testers and QA analysts

Official title Software Quality Assurance Analysts and Testers (SOC 15-1253)

Also called QA analyst, QA engineer, Software QA engineer, Software tester, Test engineer

In brief

In May 2025, BLS counted 186,740 US software testers and QA analysts, with median pay of $104,300 a year. Of the BLS counts since 2021, the highest was 203,040, in May 2023. The earliest figure here is an estimate of 500 from 1960, not measured the same way as today's count. The last full technology era for software testers and QA analysts: Cloud testing platforms, mobile device farms, API testing, 2014 to 2022.

What the sources say

Official outlook

BLS projects employment to grow 5.7% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Software testers and QA analysts: jobs and pay since 1960 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/15-1253

Chicago

Future History. "Software testers and QA analysts: jobs and pay since 1960." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/15-1253.

BibTeX

@misc{futurehistory_15_1253, author = {{Future History}}, title = {Software testers and QA analysts: jobs and pay since 1960}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/15-1253}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
19752000now
Country
2026
Known then as Software Tester / Checker (informal, embedded in programming groups)
Known then as Software Tester / QA Specialist (formalized post-Myers 1979)
Known then as QA Engineer / Test Engineer (commercial tool era)
Known then as Software QA Analyst / Test Automation Engineer / SDET
Known then as Software Quality Assurance Analysts and Testers (BLS SOC 15-1253)
Known today as Software Quality Assurance Analysts and Testers (BLS SOC 15-1253)
Projected to still be called Software Quality Assurance Analysts and Testers (BLS SOC 15-1253)
Data from the May 2025 survey
US Employment
187K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$104,300
Est.

≈ $101,626 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1956

Gerald M. Weinberg leads the first documented dedicated software testing team at IBM, assembled to test the operating system for the IBM 704 mainframe. The team's existence acknowledged a principle that would take the industry decades to absorb: the programmer who writes the code is poorly positioned to find its flaws. Weinberg later wrote extensively about the psychology of software testing and became one of the field's most influential early advocates.

Beat · 1968

The NATO Software Engineering Conference at Garmisch, Germany formally coins the phrase "software crisis" to describe the pattern of government and aerospace computing projects running massively over budget and schedule due to software defects. The conference identifies inadequate testing and quality assurance as root causes, establishing the intellectual case for QA as a non-optional investment rather than an optional polish step. The 1969 follow-on conference in Rome reinforces these findings.

Beat · 1979

Glenford J. Myers publishes "The Art of Software Testing" (Wiley, 1979). The book introduces the defining professional principle: "a successful test is one that finds a bug." This inverted the common practice of measuring success by counting tests that passed, reframing quality assurance as an adversarial discipline rather than a confirmatory one. The ACM SIGMIS Database reviewed it as the first rigorous treatment of software testing as an independent field. The same year IEEE Std 730 establishes the first formal standard for Software Quality Assurance Plans, giving QA departments an institutional framework for the first time.

Beat · 1991

Mercury Interactive launches WinRunner, the first widely adopted commercial GUI test automation tool. WinRunner's record-and-playback functionality lets testers automate Windows application testing without writing code from scratch. The tool creates the first real skill premium in the profession: testers who can write TSL scripts command higher salaries than those who cannot. Mercury Interactive would go on to acquire and build a suite of testing tools (LoadRunner, TestDirector, QuickTest Professional) that dominated enterprise QA through the 2000s until HP's $4.5 billion acquisition in 2006.

Beat · 2002

ISTQB (International Software Testing Qualifications Board) is founded in Edinburgh by eight national boards including Germany, UK, and Sweden. ISTQB launches its Foundation syllabus in 2005, establishing the first globally portable certification for software testers. By 2026, ISTQB has certified over a million testers worldwide across 135 countries. The certification represents the profession's coming-of-age: it acquired the same institutional trappings as accounting (CPA), project management (PMP), and engineering (PE) credentials, signaling that software testing was a career rather than a temporary assignment.

Beat · 2004

Jason Huggins, a ThoughtWorks engineer, creates Selenium as an internal tool for testing web applications at ThoughtWorks. Open-sourced shortly after, Selenium becomes the dominant web automation framework globally, displacing Mercury's proprietary tools in web contexts. Selenium's free availability and browser-native execution make professional-grade web automation accessible to any company, not just those with enterprise tool budgets. The Selenium WebDriver standard (2011) is eventually adopted by the W3C, and Selenium underpins hundreds of subsequent test frameworks.

Beat · 2018

The Bureau of Labor Statistics introduces SOC code 15-1253 (Software Quality Assurance Analysts and Testers) as a standalone occupational code in the 2018 Standard Occupational Classification revision, separating QA analysts from software developers (15-1252) and computer programmers (15-1251) for the first time. The formal separation was decades overdue; the profession had existed as a distinct specialty since the 1970s. The new code enables systematic BLS OEWS data collection for QA testers and makes the occupation's employment and wage trends visible in federal statistics.

Beat · 2025

Tricentis's 2025 Quality Transformation Report, surveying 2,700 technology practitioners, documents the structural bifurcation of the QA profession: entry-level manual testing roles face real displacement from AI-native platforms (Mabl, QA Wolf, Testim), while senior SDET and quality-engineering roles commanding automation, AI model validation, and strategic coverage planning are in undersupply. Median time-to-fill for senior automation roles exceeded 90 days in North America. Salaries for professionals certified in AI testing platforms commanded 18-25% premiums over peers without those credentials. The percentage of QA job postings requiring coding skills rose from 53% in 2023 to 77% in 2025, a structural shift that mirrors what happened to graphic design when desktop publishing software arrived: the low-skill tier shrinks, the high-skill tier expands and earns more.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace software testers and QA analysts? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+5.7%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 15-1253 (Software quality assurance analysts and testers): 187.6 thousand jobs in 2025, projected 198.2 thousand in 2035, a change of +10.6 thousand (+5.7%). Matrix employment counts all jobs in the occupation, including self-employed workers. The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
61.4%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 15-1253.00 Software Quality Assurance Analysts and Testers. Shown: human-rated β = 0.614, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.316 and γ (all LLM-assisted) 0.912; GPT-4-rated β 0.877. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for software testers and QA analysts

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is taking this onExecute regression test runs in CI/CD pipelines, monitoring AI-powered self-healing test execution (Testim smart locators, Functionize SmartFix) and escalating failures that adaptive healing masks rather than resolves, distinguishing test flakiness from genuine application regressions.

Execute regression test runs in CI/CD pipelines, monitoring AI-powered self-healing test execution (Testim smart locators, Functionize SmartFix) and escalating failures that adaptive healing masks rather than resolves, distinguishing test flakiness from genuine application regressions.[9],[10]

Where your edge is

Master CI/CD pipeline configuration and test observability (monitoring test run health metrics, flake rates, healing frequency) rather than manual test execution; the human role shifts to governance of what the automated system silently adapts around.

AI is sitting alongside you hereGenerate and maintain automated end-to-end test suites using AI-native platforms (Mabl, QA Wolf, Testim) and coding-agent tools (GitHub Copilot, Diffblue Cover), reviewing and curating AI-generated tests for coverage gaps and false positives before merging to CI.

Generate and maintain automated end-to-end test suites using AI-native platforms (Mabl, QA Wolf, Testim) and coding-agent tools (GitHub Copilot, Diffblue Cover), reviewing and curating AI-generated tests for coverage gaps and false positives before merging to CI.[4],[11],[8]

Where your edge is

Shift from test authorship to test strategy: define the risk matrix and coverage criteria that AI tools optimize toward; a QA engineer who can judge which failure modes matter most remains essential even when AI writes every line of test code.

AI is sitting alongside you hereRun visual regression and cross-browser compatibility testing using AI visual validation tools (Applitools Eyes, Percy), reviewing AI-flagged screenshot diffs to classify genuine regressions from intentional design changes or dynamic-content noise.

Run visual regression and cross-browser compatibility testing using AI visual validation tools (Applitools Eyes, Percy), reviewing AI-flagged screenshot diffs to classify genuine regressions from intentional design changes or dynamic-content noise.[12],[9]

Tools picking this up
Where your edge is

Develop a designer's eye for UI quality: pixel precision is now AI's job; the human role is deciding which visual changes matter for user experience, brand consistency, and accessibility compliance, not catching them manually.

Where software testers and QA analysts go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Information Security Analysts

QA engineers already probe applications for edge cases and unexpected behaviour: the same mindset that drives effective security testing. DAST, penetration testing concepts, and API security all map directly from QA skill sets. With AI-generated code introducing 2.74× more security vulnerabilities per PR (Uvik 2026), organizations need security-aware QA professionals who can validate AI-authored code before it reaches production.

What you'd add
  • · OWASP Top 10 and CWE vulnerability taxonomy
  • · Dynamic application security testing (DAST) with tools like StackHawk, Burp Suite, OWASP ZAP
  • · API security patterns: OAuth, JWT, PKCE, rate limiting, injection vectors
  • · Threat modeling (STRIDE) and security requirements analysis
  • · Security certifications: OSCP, GWEB, or CompTIA Security+ as credibility anchors
What it takesSome new skills to pick up
Tools of the era

How has the work of software testers and QA analysts changed since 1956?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Manual code tracing and desk-checking (pre-disciplinary era)

    In the earliest computing era, testing consisted of tracing through code by hand, comparing against expected outputs, or submitting batch jobs and reading printout results. There were no testing tools, no frameworks, and no methodology. Gerald Weinberg's IBM team in the late 1950s represented the most advanced practice of the era: a group of people whose explicit job was to attempt to break software before it went live. The 1968 NATO Software Engineering Conference in Garmisch, Germany formally named the "software crisis" and identified inadequate testing as a root cause of catastrophic cost overruns in government computing projects. Testing remained informal and ad hoc for most organizations through the 1970s.

  • Structured testing methodology (Myers 1979, IEEE Std 730, black-box / white-box discipline)

    The publication of Glenford Myers's "The Art of Software Testing" (1979) and the simultaneous release of IEEE Std 730 (Software Quality Assurance Plans) gave the profession its intellectual foundation. Myers introduced the distinction between black-box testing (testing behavior from the outside, without knowledge of internal structure) and white-box testing (testing code paths and logic structures). Equivalence partitioning and boundary value analysis, formalized in this era, remain standard practice today. The discipline acquired its first academic treatment, its first professional vocabulary, and the beginnings of organizational independence from development teams. ANSI/IEEE 829-1983 established standards for software test documentation, giving QA managers the paperwork scaffolding to run formal test cycles.

  • Commercial GUI test automation (Mercury WinRunner 1991, QuickTest Professional 1998)

    Mercury Interactive launched WinRunner in 1991, introducing record-and-playback GUI automation for Windows applications. For the first time, a tester could record a sequence of user interactions and replay it automatically against a new build, checking for regressions without re-executing each step by hand. QuickTest Professional (QTP), introduced by Mercury in 1998 and using VBScript for test logic, became the dominant enterprise automation platform of the early 2000s. These tools created the first real skill divide within the profession: testers who could script in TSL or VBScript commanded materially higher salaries than those who could not. HP acquired Mercury Interactive in 2006, rebranding QTP as HP Unified Functional Testing. The commercial automation era defined the "QA Engineer" as distinct from the manual tester and set the trajectory toward the SDET role.

    Effect on the work

    Commercial automation tools reduced the labor required to execute regression test suites and enabled QA teams to maintain coverage as codebases grew. However, tool fragility (record-playback scripts break when UI elements change) meant maintenance overhead was high, and the era created as many specialized jobs maintaining test scripts as it eliminated in manual testing.

  • Open-source web testing (Selenium 2004, JIRA 2002, Agile/continuous integration era)

    Selenium, released in 2004 by ThoughtWorks engineer Jason Huggins as an internal tool for testing web applications, became the dominant web testing framework globally within a decade of its open-source release. Unlike Mercury's proprietary tools, Selenium ran in real browsers and was free, which democratized web automation. Simultaneously, Agile methodologies shifted QA from a separate "test phase at the end" to a continuous responsibility integrated into each sprint. The JIRA issue tracker (Atlassian, 2002) became the universal defect management platform, and continuous integration tools (Hudson / Jenkins) began running automated test suites on every code commit. The "shift-left" principle, testing earlier in the development cycle, became the professional norm. ISTQB, founded in Edinburgh in 2002 and launching its Foundation syllabus in 2005, gave the profession its first globally recognized certification structure.

    Effect on the work

    Selenium and CI tools dramatically raised the productivity ceiling for automation engineers. A single SDET maintaining a Selenium suite could provide coverage that previously required a large manual testing team. This accelerated a bifurcation of the profession: automation-capable testers became more valuable, manual-only testers came under wage pressure.

  • Cloud testing platforms, mobile device farms, API testing (BrowserStack 2011, Postman 2014, Docker-based CI)

    Mobile computing created an entirely new testing problem: applications needed to work across hundreds of device-OS-browser combinations. BrowserStack (founded 2011), Sauce Labs (2008), and similar real-device cloud platforms let QA engineers run tests against physical devices without maintaining local device labs. Postman (2014) formalized API testing as a first-class QA discipline alongside UI testing. Docker-based containerized test environments, Jenkins pipelines with parallelized test execution, and cloud-based test infrastructure turned CI/CD from a development practice into a quality engineering discipline. "Test engineer" became a well-compensated senior engineering title rather than an entry-level checking role at leading technology companies.

  • AI-native test automation (Mabl 2017, QA Wolf, Testim, Diffblue Cover)

    Mabl, launched in 2017 and marketed as "AI-native" from its founding, represents the first generation of test platforms where machine learning is the primary engine rather than a bolt-on feature. Mabl's auto-healing automatically updates tests when UI elements change, addressing the chronic brittleness of record-and-playback systems. QA Wolf (2019) and Testim (Tricentis) generate production-grade Playwright and Appium test code from natural language prompts, promising 80% automated E2E coverage in four months. Diffblue Cover (2018) generates entire Java unit test suites autonomously, achieving 81% average line coverage versus 32% for a developer using a coding agent. These tools automate the work that occupied entry-level manual QA roles and much of the repetitive scripting work of mid-level automation engineers. The QA profession's response is bifurcating: roles focused on test execution and script maintenance face real displacement, while roles focused on test strategy, risk coverage design, and oversight of AI-generated suites are growing in both headcount and compensation.

    Effect on the work

    Tricentis research (2025) found that customers achieved faster test suite creation after adopting AI-based modules, and 85% reduction in manual sanity testing effort. A large share of application code written in 2025 was AI-generated, and 29% of developers rolled back releases due to AI errors, increasing rather than decreasing the strategic importance of quality engineering as an oversight function. Median time-to-fill for senior automation roles exceeded weeks in North America in 2025.

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Software Quality Assurance Analysts and Testers employment and pay by year

On record since1956
Latest US employment186,740 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$104,300 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+5.7% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error

In other countries

The latest figure each country's statistics agency publishes for this job. Each uses its own occupation definition, survey basis and currency, so the numbers are not directly comparable with each other or with the US figures.

  • Canada (NOC 22222)

    Employment, 2020
    10,200, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021: number of persons with employment income in NOC 22222 Information systems testing technicians, Canada, reference year 2020. A recipients basis (anyone with employment income during the year), slightly broader than a point-in-time employed count. Statistics Canada Open Licence.
    Median pay, 2020
    C$69,000, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021 (Table 98-10-0586): median employment income for NOC 22222 Information systems testing technicians, Canada, reference year 2020, full-year full-time workers (the basis comparable to the US OEWS full-time median). Statistics Canada Open Licence.
View all 8 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
1960500n/aEst., TesterWork Researched contextReport
197910,000n/aEst., Open Library Researched contextReport
199565,000$38,000Est., TJ Maher Researched contextReport
2021190,120$98,220BLS OEWS May 2021 Official statisticReport
2022196,420$99,620BLS OEWS May 2022 Official statisticReport
2023203,040$101,800BLS OEWS May 2023 Official statisticReport
2024199,800$102,610BLS OEWS May 2024 (employment) Official statisticBLS OEWS May 2024 (pay) Official statisticReport
2025186,740$104,300BLS OEWS May 2025 Official statisticReport
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