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AI & Technology

Developer Content Filter False Positive Impact Assessment

Assess how content filter false positives affect developer productivity, workflow disruption, and tool adoption decisions. Designed for developer experience researchers and tooling teams seeking actionable improvement priorities from software practitioners.

設問の例

テンプレートの内容をプレビューできます。すべての設問は公開前に自由に編集できます。

全22問・約10分
Q01
メッセージ

Welcome! This survey explores your recent experiences with content filters and false positives in developer tools. Your participation is voluntary, and you may stop at any time. There are no right or wrong answers—we are interested in your honest opinions. All responses are confidential and will be reported in aggregate only. The survey takes approximately 10 minutes. Please answer based on the last 30 days and omit any sensitive or proprietary data.

Q02
選択式

In the last 30 days, have you used any developer tools that enforce content moderation or safety filters?

  • Yes, in the last 30 days
  • No, not in the last 30 days
Q03
選択式

Which types of developer tools with content filters have you used in the last 30 days? Select all that apply.

  • AI code assistants (e.g., coding copilots)
  • Code hosting/PR checks (e.g., repo content policies)
  • Package registries with policy checks (e.g., npm, PyPI)
  • Documentation portals or knowledge bases
  • Q&A forums or developer communities
  • CI/CD or security policy gates
  • Other (please specify)
Q04
選択式

Why haven't you used developer tools with content filters in the last 30 days? Select all that apply.

  • None of my current tools apply content filters
  • I avoid tools that include filters
  • Company policy restricts such tools
  • I'm unsure which tools include filters
  • Other (please specify)
Q05
オピニオンスケール

When it comes to content filters in developer tools, which trade-off do you prefer?

スケール: 1 – 7
最小:Minimize false negatives (stricter filtering)最大:Minimize false positives (more permissive filtering)
Q06
AIインタビュー

Based on your responses in this survey, please share any additional thoughts or experiences about false positives or content filter design in developer tools.

Q07
プルダウン

What is your primary role?

  • Backend developer
  • Frontend developer
  • Full-stack developer
  • DevOps/SRE
  • ML/AI engineer
  • Security engineer
  • Engineering manager
  • QA/Testing
  • Other
Q08
メッセージ

Thank you for your time. Your feedback will help improve content filter design in developer tools and reduce the impact of false positives on developer workflows.

Q09
オピニオンスケール

How often did you encounter false positives from these content filters in the last 30 days?

スケール: 1 – 5
最小:Never最大:Very often
Q10
オピニオンスケール

If developer tools you use introduced content filters, how disruptive do you expect false positives would be to your workflow?

スケール: 1 – 7
最小:Not at all disruptive最大:Extremely disruptive
Q11
選択式

In your view, what most often causes false positives in developer tool content filters? Select all that apply.

  • Ambiguous or broad policy definitions
  • Overly sensitive detection models
  • Missing contextual signals (e.g., file type, repo trust)
  • Poor or unrepresentative training examples
  • Misclassifying code vs. natural language
  • Locale or language issues
  • Unclear UI messaging or guidance
  • Other (please specify)
Q12
プルダウン

How many years of professional software development experience do you have?

  • Less than 1 year
  • 1–3 years
  • 4–6 years
  • 7–10 years
  • 11–15 years
  • 16–20 years
  • More than 20 years
Q13
オピニオンスケール

Overall, how disruptive were the false positives you encountered in the last 30 days?

スケール: 1 – 7
最小:Not at all disruptive最大:Extremely disruptive
Q14
選択式

What informs your expectations about content filter false positives? Select all that apply.

  • Teammates' experiences
  • Industry news or reports
  • Past experiences in other tools
  • Social media or forums
  • Vendor documentation or release notes
  • Other (please specify)
Q15
ランク付け

Rank the following improvements by how much they would reduce the impact of false positives. Place the most impactful improvement first.

  1. Clearer policy definitions in tools
  2. Better detection models (precision/recall tuning)
  3. Use more context (file type, repo trust, role)
  4. Faster and more transparent appeal or override process
  5. Granular admin and user controls
  6. Improved UI messaging and guidance
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Q16
プルダウン

What is your organization size?

  • 1 (just me)
  • 2–10
  • 11–50
  • 51–200
  • 201–1,000
  • 1,001–5,000
  • 5,001+
Q17
自由回答(長文)

Briefly describe your most recent false positive from a content filter in the last 30 days. Please omit any sensitive or proprietary data.

Q18
プルダウン

Where are you primarily located?

  • North America
  • Europe
  • Latin America
  • Asia
  • Africa
  • Oceania
  • Prefer not to say
Q19
プルダウン

Approximately how long did it take to resolve your most recent false positive?

  • Less than 5 minutes
  • 5–15 minutes
  • 16–30 minutes
  • 31–60 minutes
  • 1–2 hours
  • More than 2 hours
  • It was never resolved
Q20
選択式

Which programming languages do you use most often? Select all that apply.

  • JavaScript/TypeScript
  • Python
  • Java/Kotlin
  • C/C++
  • C#/.NET
  • Go
  • Ruby
  • Rust
  • Swift/Objective-C
  • PHP
  • SQL
  • Other
Q21
選択式

After encountering the false positive, what actions did you take? Select all that apply.

  • Submitted an appeal or requested a review
  • Reworded or reformatted content
  • Used a different tool or channel
  • Waited and retried later
  • Asked a teammate/admin with different access
  • Abandoned the task
  • Other (please specify)
Q22
ランク付け

Rank the top 3 effects you experienced from false positives. Place the highest-impact effect first.

  1. Lost time
  2. Context switching
  3. Blocked release or review
  4. Lower code quality or shortcuts
  5. Frustration or stress
  6. Team coordination overhead
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含まれる機能

  • AIによる深掘り

    自由回答に合わせてAIが追加で質問し、固定のフォームでは拾えない具体的な内容を引き出します。

  • 注意確認設問

    急いだ回答や質の低い回答者を除外する仕組みを標準で備えています。

  • AIが作成する設問文

    文言、設問の順序、条件分岐をAIが調査の目的に合わせて作成します。

  • 自動レポート

    回答が集まると、テーマ、引用、わかりやすい要約が自動で作成されます。

よくあるご質問

「Developer Content Filter False Positive Impact Assessment」テンプレートにはどのような設問が含まれていますか?

すぐに使える設問が22問含まれており、最初の設問は次のとおりです:「Welcome! This survey explores your recent experiences with content filters and false positives in developer tools. Your…」・「In the last 30 days, have you used any developer tools that enforce content moderation or safety filters?」・「Which types of developer tools with content filters have you used in the last 30 days? Select all that apply.」。すべての設問は上でプレビューでき、自由に編集できます。

このアンケートの回答にはどのくらい時間がかかりますか?

回答者は通常、22問を約10分で回答し終えます。

テンプレートは編集できますか?

はい。公開前であれば、すべての設問、選択肢、順序を編集できます。設問の追加や削除のほか、調査の目的に合わせた作り直しをAIエディターに依頼することもできます。

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公開の準備はできましたか?

このテンプレートをエディターで開いてみてください。最初の回答者が目にする前に、すべてを自由に変更できます。

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