AI Automation Index

Will AI Replace Software Quality Assurance Analysts and Testers?

Task-level automation exposure for Software Quality Assurance Analysts and Testers: 30 O*NET tasks scored against their work activities, plus the simulator.

E-E-A-T Verified

Developed & audited by Gini Loh

Automation Exposure

69% weighted task index

30 of 30 tasks scored

Median Salary

$104,300 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

High

186,740 people hold this job — size, not growth

Projected 2025–35

+5.7% employment change

10,700 openings a year · BLS line item

Pay by state

BLS OEWS publishes 47 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$61,690 – $128,740

Highest medians

California $128,740

Washington $128,480

Colorado $123,690

Massachusetts $122,210

Virginia $121,590

Lowest medians

Puerto Rico $61,690

West Virginia $62,300

Louisiana $66,810

South Dakota $68,120

Vermont $76,580

State figures come from the 15-1253 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.

What the score is made of

Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.

Automation potential 70%

Importance-weighted mean of the scored tasks. This is the headline index.

Physical work 0%

Share of weighted tasks in hands-on or machine-operating work activities.

Judgment & influence 42%

Share in analysis, decisions, teaching, directing, advising, or caring for others.

Routine administration 12%

Share in documenting, recording, and administrative activities.

Rewritten by O*NET 0%

Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.

Consequence of error 12/100 Low

O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.

Constituent Task Mix

30 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.

High exposure (≥ 60% of the task is automatable) 84%

25 tasks

Medium exposure (≥ 35%) 16%

5 tasks

Low exposure (below 35%) 0%

0 tasks

How would you survive?

Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.

Simulate Upskilling →

Constituent Tasks

#14642 · Core · 70% automatable

Identify, analyze, and document problems with program function, output, online screen, or content.

text generation — Document routine operational activities from inputs

Analyze data to identify or resolve operational problems. Troubleshoot issues with computer applications or systems. Document operational activities.

#14641 · Core · 74% automatable

Document software defects, using a bug tracking system, and report defects to software developers.

text generation — Technical documentation compilation, human reviews accuracy

Compile technical information or documentation. Report maintenance or equipment problems to appropriate personnel.

#14640 · Core · 65% automatable

Develop testing programs that address areas such as database impacts, software scenarios, regression testing, negative testing, error or bug retests, or usability.

text generation — AI drafts testing routines; engineer verifies coverage

Develop testing routines or procedures.

#14638 · Core · 65% automatable

Design test plans, scenarios, scripts, or procedures.

text generation — AI drafts testing routines; engineer verifies coverage

Develop testing routines or procedures.

#14648 · Core · 80% automatable

Document test procedures to ensure replicability and compliance with standards.

text generation — Draft procedure docs from described process

Document design or development procedures.

#14653 · Core · 72% automatable

Provide feedback and recommendations to developers on software usability and functionality.

decision support — Drafts sound recommendations; human validates against real systems

Recommend changes to improve computer or information systems.

#14652 · Core · 55% automatable

Install, maintain, or use software testing programs.

physical manipulation — Software install often needs hands-on machine access

Install computer software.

#14639 · Core · 55% automatable

Test system modifications to prepare for implementation.

data analysis — AI interprets test output; human executes tests

Test computer system operations to ensure proper functioning.

#14644 · Core · 75% automatable

Create or maintain databases of known test defects.

text generation — Database creation largely automatable; human reviews

Create databases to store electronic data.

#14649 · Core · 70% automatable

Develop or specify standards, methods, or procedures to determine product quality or release readiness.

data analysis — IT performance metrics well-suited to AI

Develop performance metrics or standards related to information technology.

#14643 · Core · 83% automatable

Monitor bug resolution efforts and track successes.

data analysis — AI monitors system metrics and detects anomalies

Monitor computer system performance to ensure proper operation. Document operational activities.

#14650 · Core · 65% automatable

Update automated test scripts to ensure currency.

text generation — AI drafts testing routines; engineer verifies coverage

Develop testing routines or procedures.

#14646 · Core · 55% automatable

Participate in product design reviews to provide input on functional requirements, product designs, schedules, or potential problems.

interpersonal — Collaborative spec-setting requires human negotiation

Collaborate with others to determine design specifications or details.

#14645 · Core · 65% automatable

Plan test schedules or strategies in accordance with project scope or delivery dates.

text generation — Project plans drafted; scope and risk owned by humans

Develop detailed project plans. Develop testing routines or procedures.

#14654 · Core · 85% automatable

Monitor program performance to ensure efficient and problem-free operations.

data analysis — AI monitors system metrics and detects anomalies

Monitor computer system performance to ensure proper operation.

#14655 · Core · 64% automatable

Conduct software compatibility tests with programs, hardware, operating systems, or network environments.

data analysis — AI can run and analyze software performance tests

Test computer system operations to ensure proper functioning. Test software performance.

#14651 · Core · 45% automatable

Investigate customer problems referred by technical support.

interpersonal — Customer service is relational and accountability-bearing

Provide customer service to clients or users.

#14647 · Core · 80% automatable

Review software documentation to ensure technical accuracy, compliance, or completeness, or to mitigate risks.

text generation — Organize and check documentation accuracy largely automatable

Manage documentation to ensure organization or accuracy.

#14658 · Core · 72% automatable

Identify program deviance from standards, and suggest modifications to ensure compliance.

data analysis — AI can run and analyze software performance tests

Test software performance. Recommend changes to improve computer or information systems.

#14661 · Core · 78% automatable

Perform initial debugging procedures by reviewing configuration files, logs, or code pieces to determine breakdown source.

information retrieval — Gather technical info from documents is core

Read documents to gather technical information. Test software performance.

#14659 · Core · 65% automatable

Design or develop automated testing tools.

text generation — AI drafts testing routines; engineer verifies coverage

Develop testing routines or procedures.

#14656 · Core · 55% automatable

Install and configure recreations of software production environments to allow testing of software performance.

physical manipulation — Software install often needs hands-on machine access

Install computer software.

#14657 · Core · 67% automatable

Collaborate with field staff or customers to evaluate or diagnose problems and recommend possible solutions.

decision support — Drafts sound recommendations; human validates against real systems

Collaborate with others to resolve information technology issues. Recommend changes to improve computer or information systems.

#14660 · Core · 72% automatable

Coordinate user or third-party testing.

data analysis — AI can run and analyze software performance tests

Test software performance.

#14662 · Core · 72% automatable

Visit beta testing sites to evaluate software performance.

data analysis — AI can run and analyze software performance tests

Test software performance.

#14664 · Core · 82% automatable

Conduct historical analyses of test results.

data analysis — Trend and relationship detection in data

Analyze data to identify trends or relationships among variables.

#14663 · Core · 71% automatable

Evaluate or recommend software for testing or bug tracking.

decision support — Drafts sound recommendations; human validates against real systems

Evaluate utility of software or hardware technologies. Recommend changes to improve computer or information systems.

#21680 · Core · 70% automatable

Modify existing software to correct errors, allow it to adapt to new hardware, or to improve its performance.

text generation — Performance refactoring largely AI; human tests and approves

Modify software programs to improve performance.

#21681 · Core · 70% automatable

Recommend purchase of equipment to control dust, temperature, or humidity in area of system installation.

information retrieval — Hardware recommendations are knowledge-based with review

Provide recommendations to others about computer hardware.

#21682 · Core · 83% automatable

Store, retrieve, and manipulate data for analysis of system capabilities and requirements.

data analysis — Data preparation for analysis automatable

Assess database performance. Prepare data for analysis.

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Community Sentiment

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Method

How this was scored

1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.

Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.

Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast

See the full method →

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