Will AI replace qa engineers?
Updated
Software testers score 64 of 100 for AI exposure. The largest share of their task time, 61%, is work AI speeds up while they stay in charge. AI can take over 27% outright, and 12% stays with people.
- 27% AI can do it
- 61% AI helps
- 12% Needs a person
High exposure
Higher than 95% of the 923 occupations we scored.
Task by task
We graded 30 tasks, listed here from most to least exposed. AI can do 8 of them today, speeds up 18 and leaves 4 to people.
| Task | AI exposure |
|---|---|
| Store, retrieve, and manipulate data for analysis of system capabilities and requirements. Data storage and retrieval are routine digital tasks AI handles. | 74% AI can do it |
| Document software defects, using a bug tracking system, and report defects to software developers. Routine bug report writing and filing suits AI well. | 72% AI can do it |
| Document test procedures to ensure replicability and compliance with standards. Writing standardized test procedure documents is highly routine. | 72% AI can do it |
| Create or maintain databases of known test defects. Defect databases are structured records AI can maintain and query. | 70% AI can do it |
| Conduct historical analyses of test results. Trend analysis of past test results suits AI well. | 68% AI can do it |
| Update automated test scripts to ensure currency. Code edits to match app changes suit AI coding tools. | 66% AI can do it |
| Review software documentation to ensure technical accuracy, compliance, or completeness, or to mitigate risks. Document review for accuracy and completeness is strong AI work. | 66% AI can do it |
| Design or develop automated testing tools. AI writes test tooling code well; design choices need engineers. | 66% AI helps |
| Design test plans, scenarios, scripts, or procedures. AI drafts test cases and scripts; tester checks coverage. | 62% AI helps |
| Modify existing software to correct errors, allow it to adapt to new hardware, or to improve its performance. AI drafts code fixes; hardware behavior still needs verification. | 62% AI helps |
Show the other 20 tasks
| Task | AI exposure |
|---|---|
| Identify program deviance from standards, and suggest modifications to ensure compliance. Checking code and docs against standards is systematic pattern work. | 62% AI can do it |
| Perform initial debugging procedures by reviewing configuration files, logs, or code pieces to determine breakdown source. AI scans logs and code; confirming root cause needs engineers. | 60% AI helps |
| Identify, analyze, and document problems with program function, output, online screen, or content. AI drafts defect descriptions from outputs; tester confirms reproduction. | 58% AI helps |
| Develop testing programs that address areas such as database impacts, software scenarios, regression testing, negative testing, error or bug retests, or usability. Test program design partly generated; scenario choices need human judgement. | 50% AI helps |
| Monitor bug resolution efforts and track successes. Tracking status is routine, but chasing developers needs people. | 50% AI helps |
| Plan test schedules or strategies in accordance with project scope or delivery dates. AI drafts test plans; scheduling tradeoffs need manager input. | 50% AI helps |
| Evaluate or recommend software for testing or bug tracking. Tool comparisons are research heavy; final choice needs business input. | 50% AI helps |
| Test system modifications to prepare for implementation. Regression runs automate well; release sign off needs people. | 45% AI helps |
| Monitor program performance to ensure efficient and problem-free operations. Log and metric analysis suits AI; live monitoring needs oversight. | 45% AI helps |
| Install, maintain, or use software testing programs. Tool setup partly scripted; some environment fixes stay manual. | 42% AI helps |
| Provide feedback and recommendations to developers on software usability and functionality. Usability feedback needs real user experience and tester judgement. | 40% AI helps |
| Develop or specify standards, methods, or procedures to determine product quality or release readiness. Quality standards need organizational context and stakeholder agreement. | 40% AI helps |
| Investigate customer problems referred by technical support. Customer issue triage is text based but needs live diagnosis. | 40% AI helps |
| Conduct software compatibility tests with programs, hardware, operating systems, or network environments. Compatibility tests need physical devices and real network setups. | 35% AI helps |
| Install and configure recreations of software production environments to allow testing of software performance. Environment setup partly scripted; physical infrastructure stays manual. | 30% AI helps |
| Coordinate user or third-party testing. Drafting tester messages is easy; live coordination stays human. | 30% AI helps |
| Participate in product design reviews to provide input on functional requirements, product designs, schedules, or potential problems. Design reviews involve live debate and trade offs with stakeholders. | 18% Needs a person |
| Collaborate with field staff or customers to evaluate or diagnose problems and recommend possible solutions. Field diagnosis relies on live customer contact and site visits. | 18% Needs a person |
| Recommend purchase of equipment to control dust, temperature, or humidity in area of system installation. Equipment purchase advice depends on physical site conditions. | 10% Needs a person |
| Visit beta testing sites to evaluate software performance. Visiting beta sites requires physical presence and on site observation. | 8% Needs a person |
How software testers compare
All occupations (923)
Computer and mathematical (36)
Pay and outlook in the US
- Median pay
- $104,300 a year, May 2025
- People employed
- 186,740 in the US, May 2025
- Projected change in jobs
- +5.7% from 2024 to 2034
Skills to build
- Write SQL queries to pull defect trends from test databases
- Build and debug test automation scripts in Python or JavaScript
- Configure Linux and AWS test environments that mirror production
- Join product design reviews to flag testability problems early
- Diagnose customer-reported problems through live calls and site visits
- Hold the ISTQB Certified Tester Foundation Level credential
Safer roles nearby
5 related occupations with lower scores.
Questions about software testers and AI
Will qa engineers be replaced by AI?
Partly. About 5 in 10 working hours go to tasks AI can already do, mostly defect reports and test procedure documents. Live customer diagnosis, design reviews, and beta site visits stay with people.
Which software tester tasks is AI taking over?
AI is taking over defect documentation, defect database upkeep, and test script edits. Writing and filing bug reports in a bug tracking system is routine text work with an exposure score of 72. Maintaining known-defect databases (exposure 70) and updating automated test scripts to match app changes (exposure 66) suit AI query and coding tools.
Which software tester tasks stay human?
Beta site visits, design reviews, and customer problem diagnosis stay with people. Beta site visits (exposure 8) need someone on site watching performance firsthand. Design reviews (exposure 18) involve live debate and trade-offs with stakeholders. Diagnosing customer problems (exposure 18) relies on live contact and site visits.
How can software testers stay valuable?
Move toward the tasks that need live judgment, and take responsibility for the tools behind them. Set up test environments on Linux and AWS, join design reviews to raise testability problems early, and review AI-written Python or JavaScript test scripts before they run.
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