Q01
Long Text
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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.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
22 questions · ~4 min
Q02
Multiple Choice
In the last 30 days, have you used any developer tools that enforce content moderation or safety filters?
Q03
Multiple Choice
Which types of developer tools with content filters have you used in the last 30 days? Select all that apply.
Q04
Multiple Choice
Why haven't you used developer tools with content filters in the last 30 days? Select all that apply.
Q05
Long Text
When it comes to content filters in developer tools, which trade-off do you prefer?
Q06
AI Interview
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
Long Text
What is your primary role?
Q08
Long Text
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
Long Text
How often did you encounter false positives from these content filters in the last 30 days?
Q10
Long Text
If developer tools you use introduced content filters, how disruptive do you expect false positives would be to your workflow?
Q11
Multiple Choice
In your view, what most often causes false positives in developer tool content filters? Select all that apply.
Q12
Long Text
How many years of professional software development experience do you have?
Q13
Long Text
Overall, how disruptive were the false positives you encountered in the last 30 days?
Q14
Multiple Choice
What informs your expectations about content filter false positives? Select all that apply.
Q15
Long Text
Rank the following improvements by how much they would reduce the impact of false positives. Place the most impactful improvement first.
Q16
Long Text
What is your organization size?
Q17
Long Text
Briefly describe your most recent false positive from a content filter in the last 30 days. Please omit any sensitive or proprietary data.
Q18
Long Text
Where are you primarily located?
Q19
Long Text
Approximately how long did it take to resolve your most recent false positive?
Q20
Multiple Choice
Which programming languages do you use most often? Select all that apply.
Q21
Multiple Choice
After encountering the false positive, what actions did you take? Select all that apply.
Q22
Long Text
Rank the top 3 effects you experienced from false positives. Place the highest-impact effect first.
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.