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SRE/DevOps Toil Measurement & Automation Gap Analysis

Quantifies toil sources, automation maturity, and incident-resolution quality for SRE, platform, and DevOps teams over a 30-day period. Use to benchmark reliability operations and prioritize tooling investments.

Sample questions

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

27 questions · ~12 min
Q01
Message

Welcome to the SRE/DevOps Toil & Automation Survey. This survey asks about your experience with operational toil, automation, and reliability tooling over the last 30 days. It should take approximately 6–8 minutes to complete. Your participation is 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. Please click next to begin.

Q02
Multiple Choice

What is your primary role?

  • SRE / Production Engineer
  • Platform / Infrastructure Engineer
  • Software Engineer
  • DevOps Engineer
  • Engineering Manager
  • Other (please specify)
Q03
Multiple Choice

In the last 30 days, which activities consumed the most of your working time? Select up to 3.

  • Project / feature work
  • Incident response / on-call
  • Maintenance / operations changes
  • CI/CD and deployments
  • Troubleshooting / bug fixing
  • Meetings / coordination
  • Documentation / runbooks
  • Repetitive manual tasks
Q04
Multiple Choice

Which tooling do you actively use to manage reliability and reduce toil? Select all that apply.

  • Alerting / Monitoring (e.g., Prometheus, Datadog)
  • Incident management (e.g., PagerDuty, Opsgenie)
  • Infrastructure as Code (e.g., Terraform, Pulumi)
  • Configuration management (e.g., Ansible, Chef)
  • CI/CD orchestration (e.g., Jenkins, GitHub Actions)
  • Feature flags / progressive delivery
  • SLO / Error budget tooling
  • Runbooks / ChatOps automation
  • Change management (e.g., ServiceNow)
  • Internal developer portal (e.g., Backstage)
  • Chaos / Resilience testing
  • None of the above
  • Other (please specify)
Q05
Dropdown

Roughly how many incidents with user impact did your team experience in the last 30 days?

  • 0
  • 1–2
  • 3–5
  • 6–10
  • 11–20
  • 21+
  • Not sure / Don't track
Q06
Long Text

What single tooling change would most reduce toil for your team?

Q07
Multiple Choice

How many years have you worked in this type of role?

  • 0–1
  • 2–4
  • 5–7
  • 8–10
  • 11+
Q08
Message

Thank you for completing this survey. Your input helps us track toil patterns and prioritize the right reliability tooling investments. Results will be shared in aggregate only.

Q09
Multiple Choice

How often do you take on-call rotations?

  • Never
  • Ad hoc / occasionally
  • Weekly
  • Every 2 weeks
  • Monthly
  • Less often than monthly
Q10
Dropdown

In the last 30 days, approximately how many hours per week did you spend on repetitive manual tasks?

  • 0 hours
  • 1–3 hours
  • 4–7 hours
  • 8–12 hours
  • 13–20 hours
  • More than 20 hours
Q11
Opinion Scale

Overall, how automated are your common operations tasks today?

Scale: 17
Min:Not at all automatedMax:Fully automated
Q12
Multiple Choice

Compared to 3 months ago, how has your median time to resolve incidents changed?

  • Improved (decreased)
  • About the same
  • Worsened (increased)
  • Not sure / Don't track
Q13
AI Interview

What are the biggest blockers to automating more of your operations work next quarter?

Q14
Multiple Choice

Approximately how large is your organization?

  • 1–49 employees
  • 50–249
  • 250–999
  • 1,000–4,999
  • 5,000–19,999
  • 20,000+
Q15
Multiple Choice

In the last 30 days, which were your main sources of toil? Select up to 5.

  • Noisy or flaky alerts
  • Manual deployments
  • Brittle CI/CD pipelines
  • Environment drift or config mismatch
  • Access or permissions requests
  • Manual change approvals
  • Capacity management chores
  • Ticket handoffs or coordination
  • Limited observability or telemetry gaps
  • Flaky tests
  • Rollback or roll-forward complexity
  • Data migrations or backfills
  • Tooling integrations or gaps
  • Other (please specify)
Q16
Opinion Scale

How effective are your current tools for monitoring and alerting?

Scale: 17
Min:Not at all effectiveMax:Extremely effective
Q17
Multiple Choice

During your most significant incident in the last 30 days, what added the most toil?

  • Paging noise or alert confusion
  • Manual runbook steps
  • Access or permissions delays
  • Coordination or hand-off overhead
  • Rollback or roll-forward complexity
  • Limited data or observability gaps
  • Change approvals or governance delays
  • No significant incidents in the last 30 days
Q18
Long Text

Based on your responses in this survey, please share any additional thoughts or feelings about toil, reliability, or tooling that we didn't cover.

Q19
Multiple Choice

Approximately how large is your SRE/Platform team?

  • 1
  • 2–5
  • 6–10
  • 11–20
  • 21+
Q20
Ranking

Rank the following by how disruptive they are to your focused engineering time (1 = most disruptive).

  1. Noisy alerts / pages
  2. Manual deployments
  3. Access / permissions requests
  4. Environment setup / configuration
  5. Manual change approvals
  6. Capacity / infrastructure changes
Drag to rank
Q21
Opinion Scale

How effective are your current tools for deployment and CI/CD?

Scale: 17
Min:Not at all effectiveMax:Extremely effective
Q22
Multiple Choice

Which region best describes your primary working time zone?

  • Americas
  • EMEA
  • APAC
  • Other / Multiple
Q23
Opinion Scale

How effective are your current tools for incident management and response?

Scale: 17
Min:Not at all effectiveMax:Extremely effective
Q24
Multiple Choice

What is your work location model?

  • Remote
  • Hybrid
  • Onsite
Q25
Opinion Scale

How effective are your current tools for infrastructure provisioning and configuration?

Scale: 17
Min:Not at all effectiveMax:Extremely effective
Q26
Opinion Scale

How effective are your current tools for change management and approvals?

Scale: 17
Min:Not at all effectiveMax:Extremely effective
Q27
Dropdown

Approximately how many manual steps did you automate or remove from runbooks in the last 30 days?

  • 0
  • 1–5
  • 6–15
  • 16–30
  • 31+

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

  • Purpose-built for DevEx research: separate templates for docs findability, toolchain setup, events, and samples—not one generic 'software feedback' form
  • AI follow-ups dynamically probe developer pain points (e.g., 'You rated docs navigation poorly—can you describe what you were trying to find?') that static surveys can't replicate
  • Full transparency: every AI prompt, model, and logic branch is visible to your research team, ensuring reproducible, publishable findings
  • AI follow-ups can probe into specific technical pain points like SDK ergonomics, error message clarity, or doc search frustration
  • Templates address niche developer concerns: API rate limits, RAG system grounding quality, and integration DX — topics no competitor covers

SurveyMonkey

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What it does well

  • Expert-certified template with ready-to-use questions
  • Easily customizable with SurveyMonkey's established platform and 200+ integrations

Where it falls short

  • No AI-powered follow-up questions to probe deeper on developer-specific frustrations
  • Generic software evaluation focus—no coverage of docs quality, API ergonomics, or SDK usability
  • Static question flow cannot adapt based on developer role, experience level, or specific pain points

SurveyMonkey

Software And App Customer Feedback with NPS

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What it does well

  • Well-structured NPS methodology for benchmarking software satisfaction
  • Quick to deploy with an established scoring framework

Where it falls short

  • NPS measures loyalty but cannot surface why developers are dissatisfied with specific tooling or docs
  • No adaptive follow-up capability—misses the qualitative 'why' that drives developer churn
  • Not tailored to developer-specific workflows like API integration, CLI setup, or migration experiences

Jotform

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What it does well

  • Completely free to use with no-code drag-and-drop builder
  • 100+ integrations for data collection and report generation

Where it falls short

  • No AI follow-ups or conversational interview capabilities
  • Generic client satisfaction framing—not designed for developer personas or technical workflow feedback
  • No support for academic-grade methodology like rubric-checked scale construction or reproducible parameter logging

SurveySparrow

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A conversational UX survey template with a mobile-first design. It focuses on general product/app UX rather than developer-specific experiences and lacks domain-specific questions about API docs, SDK onboarding, or developer tooling.

What it does well

  • Conversational UI format that boosts completion rates with one-question-at-a-time flow
  • Mobile-first design with automation and recurring survey capabilities

Where it falls short

  • No AI-powered follow-up questions—conversational UI is pre-scripted, not adaptive
  • Focused on general UX, not developer experience specifics like documentation navigation, code samples, or CLI ergonomics
  • No prompt/model transparency or research reproducibility features for academic use

SurveySparrow

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A software evaluation questionnaire covering ease of usage and compatibility. While it includes logic branching and report generation, it targets IT procurement decisions rather than gathering developer experience feedback on APIs, SDKs, or documentation.

What it does well

  • Logic branching to tailor questions by respondent role
  • Report generation for synthesizing evaluation results

Where it falls short

  • Designed for software procurement evaluation, not developer experience research
  • No AI interview capabilities to probe deeply on specific pain points
  • Lacks developer-domain questions about API ergonomics, documentation quality, or setup friction

Ready to launch?

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