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Test a code change safely

Software test specialist

AI exposure does not mean job loss.

Sources have been checked. Cards are AI-assisted educational scenarios; independent expert editorial approval is still pending.

What could change in this task?

AI can suggest test-case drafts and edge cases. It can summarize test results and speed up repetitive checks.

Where does human responsibility remain?

A person sets the acceptance criteria, prioritizes risks, and remains responsible for the release decision. AI output is not accepted blindly; the test itself is verified to run as intended.

What don't we know yet?

The benefit depends on the tools' capabilities, the team's test automation, and security rules. Data access and approval processes can vary by country, sector, and software criticality.

Transferable skills

defining edge casesreading test resultswriting bug reports

Your first learning step

Do not upload real customer, employee, financial or personal data to AI tools.

  1. 20 min: Write three acceptance tests and three edge-case tests using synthetic user records with no personal information.
  2. 30 min: Run a test for a simple function; record the failed result as expected versus actual.
  3. 25 min: Ask AI for two test drafts; inspect the inputs and outputs, then revise one without approving it blindly.

Take a minute to think

What evidence would you want before releasing a test into production?

WorksheetCompare two tasks

Related tasks

Turn data into meaningful findingsResolve a technical issue safely
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General research behind this card

These studies support general changes in tasks and skills. The practice example is not a measured career outcome in the report or a forecast for Türkiye.

OECD

Artificial intelligence and the changing demand for skills in the labour market

April 10, 2024

Examines how AI changes work tasks and required skills; workers without specialist AI skills may also be affected.

Evidence scope
The verified abstract finds that AI changes the tasks and skills of the most exposed workers, even when they have no specialist AI skills. It uses multi-country job postings and is not a prediction for Türkiye.
Underlying data
Job postings covered by the publication; see the original methods section for the period.
Use / license
OECD terms · pre–July 2024

Use with attribution as a modified educational adaptation; this is not a translation of the full report and does not imply institutional endorsement.

Read the original publicationTerms of use
ILO

Generative AI and Jobs: A Refined Global Index of Occupational Exposure

May 20, 2025

Clerical and digitised tasks may be exposed; transformation is more likely than replacement of the whole job.

Evidence scope
This is a global task index using Polish classifications, worker and expert assessments, and ISCO-08 mapping. It does not measure observed job loss.
Underlying data
A 2025 task assessment and model output, not measured future job loss.
Use / license
CC BY 4.0

Use as a modified educational adaptation; it was not prepared, reviewed, or endorsed by the ILO and includes no third-party materials.

Read the original publicationTerms of use
OECD

AI and skills: What we know so far

June 5, 2026

The evidence is interpreted around digital, problem-solving, creativity, and training; it does not guarantee a universal skill or job outcome.

Evidence scope
It draws on multi-country surveys with sector and sample limitations and is not an occupation forecast for Türkiye.
Underlying data
The latest underlying evidence is from late 2024; the publication is from 2026, but the data are not 2026 data.
Use / license
CC BY 4.0

Use as a changed short educational adaptation; it does not represent OECD views, and do not use OECD logos.

Read the original publicationTerms of use