ソフトウェアQAアナリスト
コンピュータ・数学AI露出度
- データ出典: BLS公表時点: '26.08
非常に高い· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分（低い / 中程度 / 高い / 非常に高い）
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: '26.03
0.519
0.000ここに掲載された値の範囲0.745尺度・母数・出典
- データ出典: ILO公表時点: '25
0.53
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の職業小分類単位 — 同じコードの職業はすべて同じ値
当サイトの算出であり、ILOが公表した数値ではありません。当サイトがILOデータセットに結び付けた職業1,012件のうち、この値以上のものは14%です。
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
Task-level exposure
非表示の作業 18 件を表示| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Identify, analyze, and document problems with program function, output, online screen, or content.15-1253 | 0.910071.1 | 1.620068.6 |
Modify existing software to correct errors, allow it to adapt to new hardware, or to improve its performance.15-1253 | 0.190014.8 | 0.340014.4 |
Provide feedback and recommendations to developers on software usability and functionality.15-1253 | 0.07005.5 | 0.02000.8 |
Collaborate with field staff or customers to evaluate or diagnose problems and recommend possible solutions.15-1253 | 0.04003.1 | 0.01000.4 |
Monitor program performance to ensure efficient and problem-free operations.15-1253 | 0.03002.3 | 0.20008.5 |
Identify program deviance from standards, and suggest modifications to ensure compliance.15-1253 | 0.02001.6 | 0.03001.3 |
Perform initial debugging procedures by reviewing configuration files, logs, or code pieces to determine breakdown source.15-1253 | 0.01000.8 | 0.07003.0 |
Store, retrieve, and manipulate data for analysis of system capabilities and requirements.15-1253 | 0.01000.8 | 0.02000.8 |
Test system modifications to prepare for implementation.15-1253 | 0.00000.0 | 0.02000.8 |
Document software defects, using a bug tracking system, and report defects to software developers.15-1253 | 0.00000.0 | 0.01000.4 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Design test plans, scenarios, scripts, or procedures.O*NET Task ID 14638 | 1.0 |
Test system modifications to prepare for implementation.O*NET Task ID 14639 | 1.0 |
Develop testing programs that address areas such as database impacts, software scenarios, regression testing, negative testing, error or bug retests, or usability.O*NET Task ID 14640 | 1.0 |
Document software defects, using a bug tracking system, and report defects to software developers.O*NET Task ID 14641 | 1.0 |
Identify, analyze, and document problems with program function, output, online screen, or content.O*NET Task ID 14642 | 1.0 |
Monitor bug resolution efforts and track successes.O*NET Task ID 14643 | 1.0 |
Create or maintain databases of known test defects.O*NET Task ID 14644 | 1.0 |
Plan test schedules or strategies in accordance with project scope or delivery dates.O*NET Task ID 14645 | 1.0 |
Review software documentation to ensure technical accuracy, compliance, or completeness, or to mitigate risks.O*NET Task ID 14647 | 1.0 |
Document test procedures to ensure replicability and compliance with standards.O*NET Task ID 14648 | 1.0 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page