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.
샘플 질문
템플릿에 포함된 내용을 미리 확인해 보세요. 모든 질문은 설문 공개 전에 자유롭게 수정할 수 있습니다.
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
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)
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)
When it comes to content filters in developer tools, which trade-off do you prefer?
Based on your responses in this survey, please share any additional thoughts or experiences about false positives or content filter design in developer tools.
What is your primary role?
- Backend developer
- Frontend developer
- Full-stack developer
- DevOps/SRE
- ML/AI engineer
- Security engineer
- Engineering manager
- QA/Testing
- Other
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.
How often did you encounter false positives from these content filters in the last 30 days?
If developer tools you use introduced content filters, how disruptive do you expect false positives would be to your workflow?
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)
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
Overall, how disruptive were the false positives you encountered in the last 30 days?
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)
Rank the following improvements by how much they would reduce the impact of false positives. Place the most impactful improvement first.
- Clearer policy definitions in tools
- Better detection models (precision/recall tuning)
- Use more context (file type, repo trust, role)
- Faster and more transparent appeal or override process
- Granular admin and user controls
- Improved UI messaging and guidance
What is your organization size?
- 1 (just me)
- 2–10
- 11–50
- 51–200
- 201–1,000
- 1,001–5,000
- 5,001+
Briefly describe your most recent false positive from a content filter in the last 30 days. Please omit any sensitive or proprietary data.
Where are you primarily located?
- North America
- Europe
- Latin America
- Asia
- Africa
- Oceania
- Prefer not to say
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
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
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)
Rank the top 3 effects you experienced from false positives. Place the highest-impact effect first.
- Lost time
- Context switching
- Blocked release or review
- Lower code quality or shortcuts
- Frustration or stress
- Team coordination overhead
포함된 기능
AI 후속 질문
정형화된 설문이 놓치는 세부 내용을, 주관식 답변에 맞춰 AI가 심층 질문으로 끌어냅니다.
주의력 확인 장치
성의 없는 답변과 저품질 응답자를 걸러내는 내장 안전장치입니다.
AI가 작성한 문안
문구, 질문 순서, 분기 로직까지 AI가 연구 목표에 맞춰 작성합니다.
자동 리포트
응답이 모이면 주요 주제, 인용문, 이해하기 쉬운 요약이 자동으로 작성됩니다.
설문을 공개할 준비가 되셨나요?
이 템플릿을 편집기에서 열어 보세요. 첫 응답자가 보기 전에 모든 부분을 원하는 대로 바꿀 수 있습니다.
관련 템플릿
같은 카테고리의 다른 설문을 만나 보세요.
AR Virtual Try-On Realism & Purchase Confidence Study
Measures perceived realism, fit accuracy, and purchase confidence for augmented reality try-on features. Designed for e-commerce UX researchers seeking to identify AR experience gaps that drive returns and reduce conversion.
템플릿 보기Creator AI Adoption, Ethics & Disclosure Survey
Measures AI tool adoption rates, usage barriers, quality-speed tradeoffs, and credit/disclosure norms among media creators across disciplines. Suitable for creative industry researchers and platform teams studying the creator-AI relationship.
템플릿 보기AI 변경 로그의 명확성 및 도입 영향 조사
사용자가 AI 제품 변경 로그의 명확성, 유용성, 행동적 영향을 어떻게 인식하는지 측정합니다. 릴리스 커뮤니케이션을 최적화하고 기능 도입을 촉진하려는 제품 및 개발자 경험 팀을 위해 설계되었습니다.
템플릿 보기AI Bug Bounty: Scope, Fairness & Incentive Evaluation
An internal stakeholder survey evaluating scope clarity, decision fairness, and incentive effectiveness in your AI bug bounty program over the past 6 months to guide program improvements.
템플릿 보기AI Feature Adoption & Value Perception Survey
Measures user interest, perceived value, adoption barriers, and willingness to pay for AI-powered product features. Designed for SaaS product teams prioritizing their AI roadmap based on user feedback.
템플릿 보기Flight Booking Chatbot Usability & Trust Survey
Evaluates how well an airline or travel site's AI chatbot handles real booking, change, and support tasks — covering task completion, trust, and where users bail out to a human. An AI follow-up interview reconstructs exactly what happened in the respondent's most recent chatbot session, not just how they'd rate it in hindsight.
템플릿 보기