Application Modernization Checklist

1. Current State Assessment

You can’t modernize what you haven’t mapped. Start here.

All applications are inventoried with business criticality and owner assigned Technical debt is quantified and prioritised per system Current performance baselines are captured (latency, throughput, error rates) Architecture and integration dependencies are fully documented End-of-life components are identified (frameworks, OS, databases, libraries) SLA and uptime requirements are documented per system License and vendor contract obligations are reviewed Modernization ROI case is approved by business stakeholders

2. Architecture & Design

The architecture decision is the one you’ll live with the longest.

Target architecture pattern is selected (microservices, modular monolith, serverless) Domain boundaries are defined using domain-driven design or equivalent API contracts are designed with a versioning and deprecation strategy Event-driven or async messaging patterns are defined where needed Migration pattern is selected (Strangler Fig, Big Bang, incremental refactor) Security architecture is reviewed and updated for the target state Scalability, resilience, and fault-tolerance requirements are modelled Observability strategy (structured logs, metrics, distributed traces) is designed Data persistence strategy for cloud-native targets is defined

3. Code & Dependencies

Legacy code debt compounds. Address it before it migrates with you.

Language and framework versions are updated to supported LTS versions Third-party dependencies are audited for CVEs and API compatibility Dead code and unused dependencies are removed Code coverage meets the minimum agreed threshold (target ≥70%) Static code analysis is integrated into the development workflow Secrets are migrated from hardcoded values to a vault or environment variables Application configuration is externalized from code Database access layer is properly abstracted (repository pattern or ORM)

4. Data Modernization

Data schema and access patterns determine how hard the refactor actually is.

Data models are reviewed and refactored to match the target architecture Database migrations are scripted, version-controlled, and repeatable Read/write patterns are analysed for caching and optimisation opportunities Database performance bottlenecks are identified and addressed Schema backward compatibility strategy is defined for all APIs Data validation and integrity constraints are enforced at the database level Data access layer is cleanly separated from business logic Sensitive data handling is compliant with all applicable privacy regulations

5. CI/CD & DevOps

Modernization without automation creates a second maintenance burden.

All source code is in version control with a defined branch strategy Automated build pipelines run on every commit Automated test suite runs on every pull request (unit, integration, E2E) Deployment pipeline supports zero-downtime rollback Infrastructure is defined as code (Terraform, CDK, Pulumi, or equivalent) Environment parity is maintained across dev, staging, and production Feature flags are used to decouple deploy from release Security scanning (SAST, dependency vulnerability check) is in CI/CD

6. Team & Organization

Technology modernizes; without team structure change, the debt comes back.

Teams are organised around product domains, not technology layers Engineers have received cloud-native and modern architecture training Documentation is maintained in source control alongside the code Architecture decision records (ADRs) document key design choices Sprint cadence includes allocated technical debt reduction work Service ownership is assigned with clear on-call accountability Incident postmortem process is established and practiced Cross-team API contract review process is defined A named owner is accountable for overall modernization programme progress

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