Annual team cost
$3,750,000
Team size × fully loaded salary.
Maintenance budget (est.)
$1,237,500
Assumes 33% time spent on maintenance/toil (Stripe 2018).
Potential annual savings
$694,238
Annual maintenance budget × blended improvement ratio from the cited studies.
Projected DORA outcomes with these inputs: cycle time drops to 1.8 days, deployment frequency climbs to 6 releases per week, MTTR improves to 2.2 hours, and change failure rate falls to 5.0%. Use this snapshot to compare scenarios before committing to larger DevOps investments.
Inputs reflect your environment
Team size, compensation, and current delivery metrics stay local to your browser. Change them to match your reality and see how the research scales.
Improvement ratios come straight from citations
Wilkes et al. (ICSME 2023): cycle time 3.2 → 1.8 days (–43.8%), MTTR 6.5 → 2.2 hours (–66.2%). Rüegger et al. (ICSSP 2024): deployments 0.7 → 2.1 per week (+200%), change failure 12% → 5% (–58.3%). Internal calculations reuse those exact deltas.
Want the full research context? Read our evidence summary in the Engineering Metrics: A Pragmatic Analysis of What We Actually Know.
ROI is a blended proxy
Savings estimate = annual maintenance budget × blended improvement ratio. Maintenance share defaults to 33% (Stripe Developer Coefficient 2018) and the failure-rate baseline is the 12% reported in the ICSSP study. This is not a business case—just a directional gauge for internal review.
All data stays on this page
The simulator runs entirely in your browser—no form submissions, storage, or analytics hooks yet. We collect feedback manually once you volunteer an email.
| Study | Finding | Metric |
|---|---|---|
| A Framework for Automating the Measurement of DevOps Research and Assessment (DORA) Metrics | Automated CI/CD adoption across 304 open source projects reduced median cycle time from 3.2 days to 1.8 days (-43.8%). | cycle time |
| A Framework for Automating the Measurement of DevOps Research and Assessment (DORA) Metrics | Incident response automation in the same CI/CD rollout cut median MTTR from 6.5 hours to 2.2 hours (-66.2%). | mean time to recovery |
| Fully Automated DORA Metrics Measurement for Continuous Improvement | Automated DevOps tooling integration increased deployment frequency from 0.7 to 2.1 releases per week (+200%) across 37 production microservices. | deployment frequency |
| Fully Automated DORA Metrics Measurement for Continuous Improvement | In the same DevOps tooling rollout, change failure rate decreased from 12% to 5% (-58.3%). | change failure rate |
Model conservative, evidence-backed improvements to your DORA metrics. Start with your current baseline, apply peer-reviewed deltas, and share the scenario with stakeholders—no spreadsheets required.
Ground DevOps investments in peer-reviewed deltas
The simulator reuses improvement ratios from two large-scale studies. Wilkes et al. (ICSME 2023) measured how automated CI/CD and incident response reshape cycle time and MTTR across 304 open source projects, while Rüegger et al. (ICSSP 2024) documented deployment-frequency and change-failure improvements after disciplined DevOps tooling. Plug in your current metrics and the model applies those published deltas so stakeholders see a realistic envelope instead of marketing fantasy.
Every assumption is cited in the interface. If your organisation has stronger data, swap in your ratios and keep the citation trail intact so executives can audit the reasoning behind the forecast.
Worked scenario: 25 engineers investing in CI/CD
Start with the default inputs: a 25-engineer group on $150k fully-loaded salaries, shipping twice per week with a 3.2-day cycle time and 6.5-hour MTTR. The simulator projects a future where deployments triple, MTTR drops to roughly 2.2 hours, and cycle time stabilises around 1.8 days. Using Stripe’s 33% maintenance heuristic, that translates to a six-figure annual capacity release you can reinvest in roadmap work.
Share the generated URL with finance, product, or platform peers and they will see the same scenario. Copy the link into a doc, duplicate it with different baselines, and compare which squads deliver the biggest lift.
Limitations and calibration guidance
The output is directional. Teams with heavy compliance burdens or immature tooling may progress slower than the cited averages, while elite teams might already exceed them. Re-run the model each quarter with your telemetry to tighten the ranges and to demonstrate how investments compound.
We also keep the maintenance-share assumption at 33% to align with the Tech Debt calculator. If your mix of toil and refactoring differs, export the results or fork the component so the calculation reflects your environment—just keep the citations visible to maintain trust.
Citations
- Wilkes et al. – A Framework for Automating the Measurement of DORA Metrics
Source for the cycle-time and MTTR deltas after automated CI/CD adoption.
- Rüegger et al. – Fully Automated DORA Metrics Measurement
Provides the deployment-frequency and change-failure ratios used in the simulator.
- DORA State of DevOps 2023
Broader context for how elite performers operationalise engineering metrics and governance.
Related reading
- Why engineering roadmap estimation fails
Connect delivery metrics to the planning pitfalls product and platform leaders wrestle with.
- Tech debt cost calculator
Quantify maintenance budgets so you can reinvest efficiency gains intentionally.
- Monte Carlo forecasting tool
Pair probabilistic delivery timelines with your metrics uplift narrative for a full story.
Frequently Asked Questions
Questions we hear from teams evaluating this tool for their roadmap and estimation workflow.
Where do the improvement numbers come from?
Cycle time and MTTR ratios are pulled from Wilkes et al. (ICSME 2023); deployment frequency and change-failure deltas come from Rüegger et al. (ICSSP 2024). Both are cited directly in the UI.
Can I tweak the assumptions?
Yes. Export the results or fork the component to plug in your own improvement ratios or maintenance-share percentage while keeping the citation trail visible.
Does the simulator store any data?
No. Everything runs client-side. Sharing a scenario simply encodes the inputs in the URL so teammates can open the same view.
How often should I update my baseline?
Revisit the inputs quarterly. Use telemetry from your delivery platform to refresh cycle time, deployments, and MTTR so the projection stays credible.
Is this a full business case?
Think of it as a directional planning aid. Combine the projected savings with qualitative factors—risk, compliance, talent—for a complete investment proposal.
Will this become part of ScopeCone’s product?
That’s the roadmap. The research lab release lets us validate the UX and math with you before we wire it into live scenarios.
Subscribe for honest engineering metrics research
Get future ScopeCone tools, deep-dive blog posts, and research updates about measurement, CI/CD, and engineering productivity without the hype.
We respect your privacy. Unsubscribe at any time.