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- Correct the DORA 2024 thresholds table (Elite/High/Medium/Low) and the 2025 caveat (seven archetypes, report rename; DRR added in 2024) in assets/dora-metrics-reference.md to match references/metrics-and-dora.md - Fix Firefox cadence to the 2-week train and align GitLab's release day with the third-Thursday fact in references/release-process-models.md and references/release-operations-and-triage.md - Fix the dead versioning-decision-table.md link in templates/release-plan.md to point at ../assets/versioning-decision-table.md - Scope the deployment-frequency observation window to the selected environment, clamp negative change lead time at 0, and require a recovery deployment to start at or after the failed deployment finished in scripts/dora_metrics.py - Make the metrics-and-dora.md worked example internally consistent (21 total deploys, DRR = 3/21 = 14.3%) - Reword the version_bump.py docstring to describe the implemented pre-release behavior instead of unimplemented graduation - Add tests for environment-scoped window, CLT clamp, and recovery candidate ordering (114 tests pass under pytest and unittest) Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
44 lines
3.8 KiB
Markdown
44 lines
3.8 KiB
Markdown
# DORA Metrics Quick Reference
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> The five research-backed software delivery metrics, how to compute them, and the classic thresholds. Use for measuring, dashboarding, and reporting delivery performance. The metrics measure system outcomes, not individual performance — do not use them for personal evaluation.
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## The Five Metrics
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| Metric | What it measures | Formula / unit | Data source |
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|--------|------------------|----------------|-------------|
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| Deployment frequency (DF) | How often code reaches a production environment | Successful production deployments per day (or per week) | Deploy logs / CI-CD platform / GitOps sync records |
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| Change lead time (CLT) | Time from commit to running in production | Median of (deploy finished_at − commit created_at) over deployed commits; unit: hours/days | Version-control commit timestamps + deploy records |
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| Change failure rate (CFR) | Share of deployments that cause degraded service | Failed or remediated deploys ÷ total deploys × 100 (%) | Deploy ↔ incident correlation (rollbacks, hotfixes, incident tickets tied to a deploy) |
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| Failed deployment recovery time | Time to restore service after a failed deploy | Median time from failed deploy start to next successful deploy; unit: minutes/hours | Incident + deploy timeline |
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| Deployment rework rate | Share of deployments needing rework (rollback, hotfix, forward fix) | Unplanned rework deployments ÷ total deployments × 100 (%) | Deploy records flagged as unplanned |
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## Classic 2024 Thresholds (the last four-tier table)
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| Tier | Deployment frequency | Change lead time | Change failure rate | Failed deployment recovery time |
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|------|----------------------|------------------|---------------------|-------------------------------|
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| Elite | On-demand (multiple deploys/day) | Less than one day | 5% | Less than one hour |
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| High | Daily to weekly | One day to one week | 20% | Less than one day |
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| Medium | Weekly to monthly | One week to one month | 10% | Less than one day |
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| Low | Monthly to biannual | One to six months | 40% | One week to one month |
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Note the 2024 **inversion**: High shows a higher change failure rate (20%) than Medium (10%) — clusters are descriptive groupings, not a monotonic scorecard.
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> **2025 change caveat —** the DORA team **retired the Elite/High/Medium/Low tiers entirely** in the 2025 report (renamed "State of AI-assisted Software Development"), replacing them with seven qualitative archetypes built on eight measures. 2025 publishes metric *distributions*, not tiers. **Deployment rework rate** was added in 2024 as the fifth metric, not in 2025. Do not hard-code the 2024 threshold table into dashboards; treat it as a historical reference point anchored to the 2024 report.
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## Top Pitfalls
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- **PRs ≠ deploys.** Count deployments of code to production, not merged pull requests or commits.
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- **Mean vs. median.** Use the median for lead time and recovery time — the mean is skewed by rare long outliers.
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- **Repo vs. service.** Measure per deployable service, not per repository (a monorepo may contain many services).
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- **Ignoring rollbacks.** A rolled-back deploy is a failure — excluding it inflates both DF and CFR.
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- **Time-source mismatch.** Commit and deploy timestamps must be comparable (UTC, NTP-synced) or lead time is meaningless.
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- **Manual counting.** Spreadsheets drift; derive the metrics from pipelines and GitOps records automatically.
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- **Gaming the metric.** Raising DF without improving CFR or lead time just amplifies bad change.
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## Sources and Further Reading
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- DORA — research and metric definitions: https://dora.dev/
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- Accelerate (Forsgren, Humble, Kim, 2018): https://itrevolution.com/product/accelerate/
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- DORA metrics measurement guidance: https://dora.dev/research/measurement/
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- Google Cloud DORA blog (2025 tier retirement): https://cloud.google.com/blog/products/devops-sre
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