All templates
Developer & Engineering

Developer Productivity & AI Tooling Adoption Survey

Measures developer productivity, AI coding tool adoption and barriers, code quality practices, and professional growth for engineering teams. Designed for 6–8 minute completion with branching logic for AI tool users vs. non-users.

Sample questions

A preview of what’s in the template. Every question is editable before you launch.

26 questions · ~11 min
Q01
Message

Welcome to the Developer Productivity & AI Tooling Adoption Survey. This survey explores your development workflows, tools (including AI assistants), code quality practices, and professional growth. It takes approximately 12 minutes to complete. Your participation is entirely voluntary, and you may stop at any time. There are no right or wrong answers—we are interested in your honest experience. All responses are confidential and will be reported only in aggregate. By continuing, you agree to participate.

Q02
Dropdown

Which of the following best describes your primary development role?

  • Backend engineer
  • Frontend engineer
  • Full-stack engineer
  • Mobile developer
  • Data/ML engineer
  • DevOps/SRE
  • QA/Test engineer
  • Engineering manager
  • Student or hobbyist
  • Other
Q03
Multiple Choice

Which of the following best describes your current use of AI coding assistants or code-generation tools (e.g., GitHub Copilot, ChatGPT, Cursor)?

  • Yes, I currently use them
  • I tried them in the past but am not using them now
  • No, but I plan to try them
  • No, and I have no plans to try them
Q04
Ranking

Rank the following activities from most to least time spent during a typical work week.

  1. Writing new code
  2. Reviewing or debugging code
  3. Meetings and communication
  4. Planning, design, or documentation
  5. Testing and QA
  6. DevOps, deployment, or infrastructure
  7. Learning or upskilling
Drag to rank
Q05
Opinion Scale

How confident are you that the code your team shipped in the last 4 weeks meets your quality standards?

Scale: 17
Min:Not at all confidentMax:Extremely confident
Q06
Dropdown

Approximately how many hours did you spend on learning or upskilling activities in the last 2 weeks?

  • 0 hours
  • 1–2 hours
  • 3–5 hours
  • 6–10 hours
  • 11–15 hours
  • More than 15 hours
Q07
Long Text

Based on your responses in this survey, please share one example from the last month where a tool, practice, or workflow change meaningfully affected how you work.

Q08
Dropdown

How many years have you worked as a developer (professional or equivalent)?

  • Less than 1
  • 1–2
  • 3–5
  • 6–9
  • 10–14
  • 15+
Q09
Message

Thank you for completing this survey! Your responses have been recorded and will be used to improve developer workflows and tooling decisions. If you have any questions, please contact your survey administrator.

Q10
Multiple Choice

Which programming languages do you use most frequently in your current work? (Select up to 3)

  • JavaScript/TypeScript
  • Python
  • Java
  • C#
  • C/C++
  • Go
  • Ruby
  • PHP
  • Swift
  • Kotlin
  • Rust
  • SQL
  • Other
Q11
Message

Great — thanks for those details. Next, a few quick questions about your team and its engineering practices.

Q12
Opinion Scale

Compared to a typical two-week period, how would you rate your overall productivity in the last 2 weeks?

Scale: 17
Min:Much less productive than usualMax:Much more productive than usual
Q13
Opinion Scale

In the last 4 weeks, how consistently did your team conduct code reviews before merging?

Scale: 15
Min:NeverMax:Always
Q14
Opinion Scale

In the last month, how much do you feel your technical skills have grown?

Scale: 15
Min:Not at allMax:A great deal
Q15
AI Interview

Thank you for your responses so far. I'd like to ask a couple of follow-up questions to better understand your experience with developer tools and productivity. Let's start: What has been the single biggest factor—positive or negative—affecting your productivity in the last month?

Q16
Dropdown

What is your primary work arrangement?

  • Company employee
  • Independent contractor / Freelancer
  • Student
  • Other
Q17
Dropdown

How frequently have you used AI coding tools over the past 4 weeks?

  • Daily
  • Several times a week
  • About once a week
  • Less than once a week
Q18
Opinion Scale

In the last 4 weeks, how consistently did your team write automated tests for new features or changes?

Scale: 15
Min:NeverMax:Always
Q19
Dropdown

About how many people are in your organization?

  • 1 (just me)
  • 2–9
  • 10–49
  • 50–249
  • 250–999
  • 1,000–4,999
  • 5,000+
Q20
Opinion Scale

In the last 4 weeks, how have AI coding tools affected your overall productivity?

Scale: 17
Min:Significantly decreased productivityMax:Significantly increased productivity
Q21
Opinion Scale

In the last 4 weeks, how consistently did your team use CI/CD pipelines for deployments?

Scale: 15
Min:NeverMax:Always
Q22
Dropdown

About how many developers are on your immediate team?

  • 1
  • 2–4
  • 5–9
  • 10–19
  • 20+
Q23
Message

Almost done! A final section about where AI tools do and don’t fit your workflow today.

Q24
Dropdown

In which region do you primarily work?

  • Americas
  • Europe
  • Middle East & Africa
  • Asia
  • Oceania
Q25
Multiple Choice

What are the main reasons you are not currently using AI coding tools? (Select up to 3)

  • Concerns about code quality or correctness
  • Privacy, security, or compliance concerns
  • Not needed for my type of work
  • Too costly or licensing issues
  • Tried but results were not useful
  • Setup or integration effort is too high
  • Team or organizational policy restricts use
  • Other (please specify)
Q26
Opinion Scale

How likely are you to try or re-adopt AI coding tools in the next 3 months?

Scale: 17
Min:Extremely unlikelyMax:Extremely likely

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.

How it compares

We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.

Why this template

  • Includes an AI follow-up interview that asks adaptive questions based on the respondent's own prior answers, not just a static question bank
  • Uses branching logic to route AI coding tool users vs. non-users into different question paths (frequency/impact vs. barriers/reasons for non-use)
  • Combines quantitative opinion-scale tracking of productivity, code review, testing, and CI/CD consistency with an open-text reflection question for qualitative depth
  • Captures both team/org context (team size, org size, role, tenure) and individual skill-growth and upskilling-hours data in one 6-8 minute flow

SurveySparrow

Employee Productivity Questionnaire Template Online

This is a general employee productivity questionnaire template, not specific to developers, AI coding tools, or code quality practices. It's a fielding-ready static template that a team could adapt, but it lacks the developer-specific branching and AI-tooling focus of our template. Useful as a generic productivity-measurement starting point rather than a purpose-built engineering survey.

What it does well

  • Ready-to-use template requiring no build-from-scratch setup
  • Backed by SurveySparrow's broader survey distribution and analytics tooling
  • Applicable across various roles, not locked to a single job function

Where it falls short

  • No apparent branching logic to separate AI tool users from non-users or developers from other employees
  • Static question set with no adaptive AI follow-up interview to probe individual responses further
  • No visible mechanism for automated per-response quality scoring or transparent prompt methodology

Ready to launch?

Open this template in the editor. Every part is yours to change before the first respondent sees it.

Related templates

More studies from the same category.

See all
Developer & Engineering

Edge Computing Reliability & Incident Response Benchmark

Benchmarks edge SLO/SLA maturity, failure handling patterns, and release safeguards for DevOps, SRE, and platform engineering teams managing edge workloads.

View template
Developer & Engineering

Developer Documentation Experience Assessment

Measures documentation usability, findability, content clarity, and code accuracy based on a developer's recent session. Designed for DX and documentation teams seeking actionable feedback to prioritize improvements.

View template
Developer & Engineering

Developer Documentation Findability & Navigation UX Survey

Evaluates how easily developers can find, navigate, and understand technical documentation. Measures discoverability, search quality, information architecture fit, and terminology clarity to prioritize documentation UX improvements.

View template
Developer & Engineering

Developer Experience Survey: Docs, Samples & Events

Measures developer satisfaction and outcomes across documentation, code samples, and community events to surface actionable improvement priorities for developer relations and product teams.

View template
Developer & Engineering

API Deprecation & Migration Experience Survey

Measures developer sentiment toward API deprecation timelines, guidance clarity, and migration burden to inform improvements in API lifecycle communication and support practices.

View template
Developer & Engineering

Software Tool Evaluation & Adoption Fit Survey

Captures how a software tool actually performed during a trial, pilot, or proof-of-concept — usability, integrations, support, and value for cost — and ranks which factors matter most to the decision. An AI follow-up interview reconstructs the real story behind the adoption call, including near-misses and blockers. Built for engineering, IT, and product teams running a formal tool evaluation.

View template