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Junior Quality Assurance Analyst

Based on 10 assessments

37% Moderate risk

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

Raw potential
68%
Realistic risk
37%
Research benchmark ?
61%

Raw potential = I/O automation ceiling. Realistic risk = adjusted for informal knowledge and social context. Research benchmark: Eloundou et al. (2023)

Distribution across 10 profiles. Middle half of Junior Quality Assurance Analysts score between 33% and 40%.

0% 50% 100%
p10 · 33%
42% · p90
On-screen work 55%

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

In-person + screen 34%

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 Junior Quality Assurance Analyst, ordered by automation exposure. Tab between them to see how task mix drives the score difference.

Task Time Type Exposure
Running automated test scripts and test suites against software builds, monitoring for failures and logging results
26% DD 58%
Reporting and triaging bugs: filling out bug tickets, assigning severity levels, communicating with developers about issues
deep expertise
24% AD 10%
Reviewing code changes or pull requests for testability and ensuring test coverage meets standards
deep expertise social element
18% DD 30%
Manually testing features by following test cases, clicking through UI workflows, and documenting bugs with screenshots and steps to reproduce
17% AD 39%
Attending standup meetings, sprint planning, and stakeholder calls to discuss testing status and blockers
deep expertise
9% AA 4%
Writing and updating test cases and test plans based on requirements and specification documents
deep expertise social element
1% DD 27%
Investigating failed tests, analyzing logs, and determining whether failures are product bugs or test environment issues
deep expertise social element
1% DD 29%

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