Will AI replace Software Quality Assurance Analysts and Testers?
On paper, AI could touch ~88% of the work in Software Quality Assurance Analysts and Testers — and unlike most jobs, it's already showing up in the real workday, not just the theory.
O*NET-SOC 15-1253
How your 27 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a high share of this job's tasks (~88%). By late 2025, real-world AI use had reached about 52% of its task activity (already common). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
The signals here line up
Theoretical reach (~88%), real-world use (~52%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 27 tasks, ratedBased on real task-level AI scores — click to collapse
- Identify, analyze, and document problems with program function, output, online screen, or content.
- Document software defects, using a bug tracking system, and report defects to software developers.
- Develop testing programs that address areas such as database impacts, software scenarios, regression testing, negative testing, error or bug retests, or usability.
- Design test plans, scenarios, scripts, or procedures.
- Document test procedures to ensure replicability and compliance with standards.
- Provide feedback and recommendations to developers on software usability and functionality.
- Install, maintain, or use software testing programs.
- Test system modifications to prepare for implementation.
- Create or maintain databases of known test defects.
- Develop or specify standards, methods, or procedures to determine product quality or release readiness.
- Monitor bug resolution efforts and track successes.
- Update automated test scripts to ensure currency.
- Plan test schedules or strategies in accordance with project scope or delivery dates.
- Monitor program performance to ensure efficient and problem-free operations.
- Conduct software compatibility tests with programs, hardware, operating systems, or network environments.
- Review software documentation to ensure technical accuracy, compliance, or completeness, or to mitigate risks.
- Identify program deviance from standards, and suggest modifications to ensure compliance.
- Perform initial debugging procedures by reviewing configuration files, logs, or code pieces to determine breakdown source.
- Design or develop automated testing tools.
- Install and configure recreations of software production environments to allow testing of software performance.
- Conduct historical analyses of test results.
- Evaluate or recommend software for testing or bug tracking.
- Participate in product design reviews to provide input on functional requirements, product designs, schedules, or potential problems.
- Investigate customer problems referred by technical support.
- Collaborate with field staff or customers to evaluate or diagnose problems and recommend possible solutions.
- Coordinate user or third-party testing.
- Visit beta testing sites to evaluate software performance.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.