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Developer & Engineering

DevOps Reliability & Incident Response Assessment

Benchmarks uptime, incident response, on-call burden, error handling, and SLA priorities across engineering teams. Designed for SREs, DevOps engineers, and software developers managing production systems.

Sample questions

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

24 questions · ~11 min
Q01
Message

Welcome! Thank you for participating in this survey on DevOps reliability and incident response practices. This survey takes approximately 7–10 minutes. 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 and opinions. All responses are confidential and will be reported in aggregate only.

Q02
Multiple Choice

How often does your team deploy changes to production?

  • Multiple times per day
  • Daily
  • Weekly
  • Every 2 to 4 weeks
  • Monthly or less
Q03
Dropdown

In the past 30 days, approximately how many user-impacting incidents did your team handle?

  • 0
  • 1–2
  • 3–5
  • 6–10
  • 11–20
  • More than 20
Q04
Opinion Scale

In the past 90 days, how often has your team experienced cascading failures or dependent-service outages?

Scale: 15
Min:NeverMax:Very frequently
Q05
Opinion Scale

How confident are you that error handling is robust across your team's critical user and system paths today?

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

Rank the following SLA/SLO dimensions by importance to your team, from most to least important.

  1. Availability (uptime %)
  2. Request latency targets
  3. Error rate / error budget
  4. Data freshness or latency targets
  5. Recovery time objective (RTO)
  6. Recovery point objective (RPO)
Drag to rank
Q07
AI Interview

We'd like to explore your reliability and SLA experiences in a bit more depth. An AI moderator will ask you a couple of follow-up questions based on your earlier responses.

Q08
Long Text

If you could trade performance or features for greater stability, what would you change first, and why?

Q09
Multiple Choice

What is your primary role?

  • Software engineer (IC)
  • Tech lead / Engineering manager
  • SRE / DevOps / Platform engineer
  • Data / ML engineer
  • QA / Testing
  • Architect
  • Product manager
  • Other
Q10
Message

Thank you for completing this survey! Your input will help prioritize the reliability outcomes that matter most to engineering teams. All results will be reported in aggregate only.

Q11
Multiple Choice

Are you currently part of an on-call rotation for production services?

  • Yes
  • No
Q12
Dropdown

In the past 30 days, approximately how many pages or high-priority alerts did you personally receive?

  • 0
  • 1–5
  • 6–15
  • 16–30
  • 31–60
  • More than 60
Q13
Opinion Scale

In the past 90 days, how often has your team experienced degraded response times or latency spikes noticeable to users?

Scale: 15
Min:NeverMax:Very frequently
Q14
Opinion Scale

Overall, how useful are your production alerts during incidents?

Scale: 17
Min:Not at all usefulMax:Extremely useful
Q15
Opinion Scale

How well does your team currently meet its primary SLA/SLO targets?

Scale: 17
Min:Not at all wellMax:Extremely well
Q16
Multiple Choice

How many years of professional software experience do you have?

  • 0–1
  • 2–4
  • 5–9
  • 10–14
  • 15+
Q17
Ranking

Rank the following on-call pain points from most to least painful.

  1. Noisy or low-signal alerts
  2. Runbook gaps or outdated steps
  3. Slow debugging due to limited traces/logs
  4. Flaky deployments or rollbacks
  5. Third-party instability
Drag to rank
Q18
Opinion Scale

In the past 90 days, how often has your team experienced deployment rollbacks or failed releases?

Scale: 15
Min:NeverMax:Very frequently
Q19
Long Text

Please describe the most important error-handling gap you noticed in the past 90 days. What was its impact, and how was it addressed (if at all)?

Q20
Multiple Choice

Approximately how large is your company?

  • 1–10
  • 11–50
  • 51–200
  • 201–1,000
  • 1,001–5,000
  • 5,001+
Q21
Opinion Scale

In the past 90 days, how often has your team experienced data inconsistencies or silent failures?

Scale: 15
Min:NeverMax:Very frequently
Q22
Dropdown

Which industry best describes your organization?

  • SaaS / B2B software
  • Consumer internet
  • Financial services / Fintech
  • Healthcare / Life sciences
  • Gaming
  • Media / Entertainment
  • Retail / E-commerce
  • Industrial / IoT
  • Government / Public sector
  • Other
Q23
Multiple Choice

Where are you primarily located?

  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle East / Africa
Q24
Multiple Choice

What is the typical size of the team responsible for your primary service or system?

  • 1–3
  • 4–7
  • 8–15
  • 16+

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

  • Goes beyond single-incident logging to benchmark team-wide reliability patterns: deployment frequency, on-call rotation status, 30-day incident and page/alert counts, and ranked on-call pain points.
  • Uses opinion-scale questions to quantify 90-day frequency of cascading failures, degraded response times, rollbacks, and data inconsistencies, plus confidence in error handling and usefulness of alerts.
  • Includes a dedicated AI follow-up interview that adaptively probes reliability and SLA experiences in more depth, alongside open-text questions on error-handling gaps and stability trade-offs.
  • Captures ranked SLA/SLO priorities and role/company demographics (role, experience, company size, industry, location, team size), with automated per-response quality scoring and an auto-generated report — available on our free tier or $50/mo Business plan.

SurveySparrow

Manage IT Incident Reporting with Software Incident Report Form

A conversational-style form for logging individual software incidents as they occur, not a broader survey benchmarking team reliability practices or SLA priorities. It's a fielding-ready template, but scoped to single-incident capture rather than aggregate assessment across engineers.

What it does well

  • Conversational, one-question-at-a-time format that SurveySparrow is known for
  • Quick to deploy for capturing individual incident details
  • Likely integrates with SurveySparrow's broader survey/workflow tools

Where it falls short

  • No adaptive AI follow-up interview to probe deeper into root causes or reliability practices
  • No ranking or opinion-scale structure to benchmark on-call burden or SLA priorities across a team
  • No automated quality scoring or auto-generated analytical report

Typeform

Software Incident Report Form Template

A polished, static form for reporting a single software incident, useful for intake/logging but not designed to assess on-call burden, cascading failure frequency, or SLA/SLO priorities across a team. It's a ready-to-use template, but narrower in scope than a full reliability assessment.

What it does well

  • Clean, mobile-friendly interface typical of Typeform
  • Conditional logic support for routing incident details
  • Easy to embed in internal tools or ticketing workflows

Where it falls short

  • No voice AI or adaptive AI interview component to explore incident context beyond fixed fields
  • No mechanism for benchmarking recurring patterns (cascading failures, rollback frequency) over a time window
  • No transparent prompt methodology or automated report synthesis

Ready to launch?

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

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