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Quality Assurance Engineer

Based on 10 assessments · 1 from real users

46% Moderate risk

Average realistic automation risk across all Quality Assurance Engineer profiles in the dataset.

Raw potential
73%
Realistic risk
46%

Raw potential = I/O automation ceiling. Realistic risk = adjusted for informal knowledge and social context.

Distribution across 10 profiles. Middle half of Quality Assurance Engineers score between 42% and 49%.

0% 50% 100%
p10 · 39%
56% · p90
On-screen work 67%

Done entirely on a computer. High AI exposure — these tasks are already in the automation zone.

In-person + screen 22%

Physical sensing, digital output — e.g. interviewing someone then writing a report. Partially protected.

Computer + action 0%

Computer input, real-world output — needs someone to act on it, not just software.

Fully in-person 11%

No computer required. Furthest from automation — the strongest human advantage.

3 synthetic profiles for a Quality Assurance Engineer, ordered by automation exposure. Tab between them to see how task mix drives the score difference.

Task Time Type Exposure
Writing and executing automated test scripts (unit tests, integration tests, regression suites)
28% DD 91%
Attending stand-ups, sprint planning, and discussing quality strategy with product and engineering teams
deep expertise
26% AA 0%
Manual exploratory testing and edge-case discovery on new features
deep expertise social element
26% AD 17%
Analyzing test results, debugging failures, and logging defect reports with reproduction steps
9% DD 52%
Maintaining and updating test frameworks, test data, and CI/CD pipeline configurations
6% DD 66%
Reviewing code changes and test coverage with developers; suggesting test improvements
some context needed
0% DD 27%
Creating test plans and defining acceptance criteria for new features or releases
deep expertise
0% AD 12%

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