A complete view of all roadmap capabilities, each mapped to a single primary phase and milestone.
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Team-wide DoD template applied to >= 90% of work items before sprint planning.
NFRs (performance, security, reliability) explicitly tracked in >= 70% of epics.
STRIDE checklist applied to >= 60% of features touching sensitive data or external integrations.
>= 80% of retrospective action items tracked to closure with owner and due date.
Team tracks velocity over >= 3 sprints and forecasts completion dates for epics with +/- 1 sprint accuracy.
>= 95% of code changes follow trunk-based development or GitHub flow (feature branches < 2 days old).
>= 90% of PRs have checklist completed before merge (tests, docs, security, performance).
>= 80% of PRs receive first review within 4 business hours.
>= 85% of commits follow conventional commit format (type(scope): description).
>= 40% of complex features (>= 5 story points) developed using pair or mob programming.
>= 70% of modules have README with setup, usage, and architecture notes. Code review checks readability.
>= 90% of repos use org-wide CI template with lint, test, build, scan stages.
100% of builds produce semantically versioned artifacts (semver) pushed to artifact registry.
>= 80% of builds generate Software Bill of Materials (SBOM) in SPDX or CycloneDX format.
100% of builds run SAST (code scan) and SCA (dependency scan) with >= HIGH severity blocking merge.
>= 95% of commits scanned for leaked secrets (API keys, passwords) pre-commit and in CI.
>= 80% of CI pipeline runs complete in < 10 minutes from commit to pass/fail result.
>= 70% code coverage for unit tests with branch coverage tracked and enforced in CI.
>= 60% of services have integration tests covering critical API endpoints and database interactions.
>= 50% of critical user journeys covered by automated E2E tests (Playwright, Cypress).
>= 70% of tests use factories or builders for test data (no hardcoded magic values).
>= 90% of flaky tests detected and fixed within 1 sprint. Flaky rate < 2%.
>= 70% of test suites run tests in parallel, reducing total test time by >= 50%.
100% of releases follow semver (MAJOR.MINOR.PATCH) with automated changelog generation.
Documented branching model applied to >= 80% of repositories with branch protection and merge requirements
Automated changelog generation using Conventional Commits for >= 90% of releases
RBAC-based release approval requiring >= 2 approvers (tech lead + product owner or SRE) for production releases
Standardized release notifications to stakeholders (dev, ops, product, support) for >= 80% of production releases
Centralized artifact registry storing >= 90% of release artifacts with version tagging and retention policies
>= 90% of merges to main auto-deploy to dev/staging environments within 15 minutes.
100% of production deployments require manual approval with >= 2 reviewers (change advisory).
>= 80% of infrastructure config identical across dev/staging/prod (IaC templates shared).
>= 95% of deployments can rollback to previous version in < 5 minutes using automation.
>= 80% of deployments achieve zero downtime using rolling updates or blue-green strategy.
>= 70% of infrastructure managed via IaC (Terraform, Pulumi, CloudFormation) in version control.
>= 80% of critical services have runbooks for deployment, incident response, and disaster recovery.
>= 90% of production services have defined on-call rotation with < 15min incident response SLA.
>= 70% of stateless services have horizontal autoscaling based on CPU/memory or custom metrics.
>= 90% of stateful services (databases, volumes) have automated backups with tested recovery procedures.
>= 90% of services send structured logs to centralized platform (ELK, Loki, CloudWatch) with retention >= 30 days.
>= 80% of services expose RED metrics (Rate, Errors, Duration) in Prometheus/StatsD format.
100% of services expose /health and /ready endpoints for liveness and readiness probes.
>= 80% of services have alerting for high error rate (>= 5% 5xx), high latency (p95 >= 1s), and down status.
>= 60% of user-facing services have defined SLOs with >= 99% availability target and <= 500ms latency target.
>= 80% of services have Grafana/Datadog dashboards showing RED metrics, resource usage, and business KPIs.
>= 80% of planning templates integrate OPA/Kyverno policies validating security, compliance, cost constraints.
>= 70% of features auto-analyzed for threats using STRIDE templates integrated into planning workflow.
>= 85% of compliance requirements auto-tracked with evidence artifacts linked to work items (SOC2, HIPAA, PCI).
>= 75% of backlog items auto-scored for risk (security, technical debt, business impact) informing prioritization.
>= 90% of changes touching regulated systems (PII, PHI, PCI) require automated regulatory checklist approval.
>= 90% of engineers complete secure coding training annually with certification required for sensitive code changes.
100% of PRs scanned with SAST (Semgrep, SonarQube) blocking >= MEDIUM issues, custom rules for org-specific patterns.
>= 95% of dependency updates auto-approved if CVE-free and passing tests, CRITICAL CVEs fixed within 48hrs.
>= 90% of secrets (API keys, tokens) auto-rotated every 90 days with expiration monitoring and alerts.
>= 80% of dependencies verified using SLSA provenance, signature verification, or checksum validation.
100% of production artifacts cryptographically signed using cosign or similar tool with signature verification enforced.
>= 80% of builds generate SLSA Level 2+ provenance with builder identity, materials, and build metadata.
>= 90% of pipelines use immutable build environments, least-privilege service accounts, audit logging enabled.
>= 80% of builds use multi-layer caching (dependencies, intermediate artifacts) reducing build time by >= 40%.
100% of container images scanned for OS vulnerabilities, malware, misconfigurations with >= HIGH blocking deployment.
>= 70% of service-to-service integrations use contract tests (Pact) to prevent breaking changes.
>= 60% of critical APIs have automated performance tests with latency/throughput gates in CI pipeline.
>= 70% of web apps scanned with DAST (OWASP ZAP, Burp) in staging environment weekly with findings tracked.
>= 50% of critical business logic code (payment, auth, data processing) uses mutation testing to validate test quality.
>= 60% of user-facing pages have automated visual regression tests (Percy, Chromatic) catching UI issues.
>= 80% of new features deployed behind feature flags with automated cleanup of flags older than 90 days.
Coordinated multi-service release scheduling with dependency mapping for >= 70% of cross-service releases
Real-time dashboard tracking release pipeline health (lead time, failure rate, MTTR) for >= 80% of releases
Automated dependency graph tracking service-to-service version requirements for >= 80% of microservices
Automated rollback tests executed for >= 70% of releases in non-production environments before production deployment
Historical release analytics tracking trends (velocity, quality, cycle time) over >= 6 months with automated reporting
>= 70% of production deployments use canary strategy (10% traffic -> 50% -> 100%) with automated rollback on error spike.
>= 80% of deployments managed via GitOps (ArgoCD, Flux) with Git as single source of truth for desired state.
>= 70% of deployments use automated traffic shifting with SLO monitoring at each phase (Flagger, Argo Rollouts).
>= 60% of stateless services support instant blue-green cutover with automated traffic switching and rollback capability.
>= 60% of critical services undergo monthly chaos experiments (pod failures, network latency, resource exhaustion).
>= 80% of critical services have automated DR failover tested quarterly with RTO < 1hr and RPO < 15min.
>= 75% of service-to-service calls protected by circuit breakers (Istio, Envoy, Resilience4j) preventing cascade failures.
>= 80% of public APIs have adaptive rate limiting protecting against traffic spikes and abuse.
>= 70% of services implement degraded mode (serve cached data, disable non-critical features) during dependency failures.
>= 85% of services instrumented for distributed tracing (Jaeger, Tempo) with trace sampling >= 10% of requests.
>= 80% of services track error budgets monthly with alerts when 50% budget consumed and deployment freezes at 90%.
>= 60% of critical metrics use ML anomaly detection (DeepAR, ARIMA) for dynamic thresholds instead of static alerts.
>= 70% of services expose business KPIs (orders/min, revenue, conversions) in observability platform alongside technical metrics.
>= 80% of log queries use structured log fields with indexed tags for <3 second query response on 30-day data.
>= 60% of user stories partially generated by AI (GPT, Copilot) from requirements, with acceptance criteria and test scenarios.
>= 75% of epic completion forecasts use ML models trained on historical velocity, complexity, team composition with +/- 0.5 sprint accuracy.
>= 70% of stories auto-analyzed for risk using NLP on description, dependency graph analysis, historical incident correlation.
>= 85% of work items auto-validated for compliance requirements using NLP policy matching and evidence verification.
>= 70% of backlog auto-prioritized using multi-factor ML: business value, risk, dependencies, team capacity, market trends.
>= 80% of PRs analyzed by AI reviewer (Copilot, CodeGuru) providing automated feedback on code quality, security, performance.
>= 60% of new functions have AI-generated unit tests with edge cases, covering >= 80% of branches.
>= 70% of merge conflicts auto-resolved by AI with human review, reducing merge time by >= 50%.
>= 65% of code modules receive quarterly AI refactoring analysis identifying duplication, complexity, design pattern opportunities.
>= 75% of code changes analyzed by LLM for context-aware security issues beyond pattern matching.
>= 70% of builds use ML-optimized strategies (predictive test selection, intelligent caching) reducing time by >= 60%.
>= 75% of build failures predicted before execution based on code patterns, dependency changes, historical data.
>= 80% of CI jobs use ML-driven resource allocation (CPU, memory) based on job type, historical usage, cost optimization.
>= 60% of flaky tests auto-fixed by AI: add waits, fix race conditions, stabilize selectors, with >= 80% success rate.
>= 80% of test suites use AI-optimized parallelization grouping tests by execution time, resource needs, dependencies.
>= 70% of features have AI-generated test scenarios from requirements, covering edge cases and negative paths.
>= 80% of PRs run only affected tests (ML predicts impact) reducing test time by >= 70% while maintaining 99% defect detection.
>= 65% of broken E2E tests auto-repaired by AI: update selectors, adjust waits, fix assertions, with >= 75% success rate.
>= 75% of tests use AI-generated realistic test data (names, addresses, transactions) maintaining privacy and edge case coverage.
>= 60% of chaos experiments use ML to select targets, predict blast radius, auto-tune intensity for maximum learning.
Automated risk assessment for >= 85% of releases using change analysis (code churn, affected services, deployment time, on-call availability)
Data-driven release scheduling optimizing for low-traffic windows, on-call availability, and historical success rates for >= 75% of releases
Automated blast radius limiting for >= 80% of releases using traffic splitting, geo-routing, or tenant isolation
Automated release orchestration coordinating multi-service deployments, health checks, and rollback decisions for >= 70% of coordinated releases
>= 85% of deployments auto-scored for risk using code diff analysis, service dependencies, time-of-day, historical incidents.
>= 75% of deployments use ML-optimized rollout plan: traffic split percentages, phase durations, rollback thresholds.
>= 80% of deployments monitored by ML for early failure signals, predicting rollback need 5-10min before SLO breach.
>= 70% of deployments auto-scheduled by AI for optimal windows based on traffic patterns, team availability, change frequency.
>= 85% of deployments protected by ML auto-rollback detecting multi-metric anomalies (errors, latency, business KPIs).
>= 70% of known incident patterns auto-remediated: restart pods, clear cache, scale resources, with >= 85% success rate.
>= 80% of services use ML-based predictive scaling anticipating load 10-30min ahead based on patterns, events, trends.
>= 75% of alerts auto-prioritized and correlated by AI reducing alert noise by >= 60% and improving MTTA by >= 40%.
>= 65% of services auto-tune configuration (thread pools, caches, timeouts) using RL agents optimizing latency, throughput, cost.
>= 80% of infrastructure capacity planned using ML forecasting 3-6 months ahead with +/- 15% accuracy.
>= 75% of incidents predicted 15-30min before occurrence based on leading indicators, preventing >= 60% from impacting users.
>= 70% of incidents have AI-suggested root cause with >= 80% accuracy based on trace, log, metric correlation.
>= 80% of alerts use adaptive thresholds auto-tuned weekly based on seasonal patterns, growth trends, false positive feedback.
>= 65% of services have AI-generated dashboards auto-selecting relevant metrics, optimal visualizations, anomaly highlighting.
>= 75% of recurring log patterns auto-categorized by AI with actionable insights: error trends, performance degradation signals.