Software Architecture Review: Evaluation Criteria

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Software architecture is the set of decisions that are expensive to change later. A poorly designed database schema can take months to migrate. A tightly coupled monolith can take years to decompose. A missing security boundary can take one incident to become a career-ending data breach. Architecture reviews exist to evaluate these structural decisions before they become permanent constraints — or to assess existing systems before investing further in them.

This guide provides a structured checklist for evaluating software architecture across six dimensions, with specific criteria, scoring guidance, and the anti-patterns that should trigger immediate concern.

The Review Process

Before the review

1. Gather documentation: Architecture diagrams (C4 model preferred), data flow diagrams, deployment topology, API contracts, and any Architecture Decision Records (ADRs).
2. Collect metrics: Current traffic patterns, error rates, deployment frequency, incident history, and performance baselines.
3. Define the review scope: The entire system, a specific subsystem, or a specific quality attribute (e.g., “can this architecture handle 10x current load?”).
4. Schedule interviews: 30-minute sessions with the tech lead, senior developers, DevOps engineer, and product owner.

During the review

Work through each dimension of the checklist below. For each criterion, score on a 1-5 scale:

Score	Meaning
5	Excellent — best practice implementation
4	Good — solid implementation with minor improvements possible
3	Adequate — meets current needs but will not scale
2	Weak — significant gaps that create risk
1	Critical — immediate action required

After the review

Produce a report with:

• Scores by dimension and overall
• Top 5 strengths (what the team is doing well)
• Top 5 risks (prioritized by severity and likelihood)
• Recommendations with effort estimates (quick wins vs strategic investments)
• A summary slide for executive stakeholders

Dimension 1: Scalability

Can the architecture handle growth in users, data volume, and traffic without fundamental redesign?

Horizontal scaling

• Can the application scale horizontally by adding more instances? (vs. only scaling vertically by adding CPU/RAM)
• Is application state externalized? (sessions in Redis/DB, not in-memory)
• Are there single points of failure? (single database, single service instance, single load balancer)
• Can components scale independently? (the API layer can scale without scaling the batch processing layer)

Data scaling

• Is the database architecture prepared for growth? (partitioning strategy, read replicas, connection pooling)
• Is there a caching strategy? (what is cached, where, what is the invalidation policy?)
• Are large datasets handled with pagination and streaming rather than full loads?
• Is there a data archival strategy for historical data that is rarely accessed?

Traffic handling

• Can the system handle 3-5x current peak traffic? (load test results)
• Is there an auto-scaling policy? (what metric triggers scaling, what are the limits?)
• Are there rate limiting and throttling mechanisms to protect against traffic spikes?
• Is there a graceful degradation strategy? (what features are disabled under extreme load?)

Red flag: If the answer to “what happens if traffic doubles?” is “we add a bigger server,” the architecture has a scalability ceiling.

Dimension 2: Security

Does the architecture protect data, enforce access control, and minimize attack surface?

Authentication and authorization

• Is authentication centralized? (single identity provider, not per-service auth logic)
• Is authorization enforced at every layer? (API gateway, service layer, database)
• Is the principle of least privilege applied? (services and users have minimum necessary permissions)
• Are API keys, tokens, and secrets managed through a vault, not hard-coded or in environment files?

Data protection

• Is data encrypted in transit? (TLS 1.2+ for all communications)
• Is sensitive data encrypted at rest? (PII, financial data, health records)
• Is there a data classification policy? (which data is sensitive, what protections apply)
• Are data retention and deletion policies implemented? (GDPR right to be forgotten)

Attack surface

• Are external-facing APIs protected by rate limiting, input validation, and WAF?
• Are internal service-to-service communications authenticated? (mTLS, service mesh)
• Is the dependency supply chain monitored? (known vulnerabilities in third-party libraries)
• Is there a security incident response plan? (detection, containment, recovery, communication)

Red flag: If the development team says “security is handled by the firewall,” the architecture lacks defense in depth.

Dimension 3: Maintainability

Can the team understand, modify, and extend the system without excessive effort or risk?

Code organization

• Is the codebase organized by domain/feature rather than by technical layer?
• Are boundaries between modules/services clear and enforced? (no circular dependencies)
• Is there a consistent coding standard followed across the codebase?
• Is the dependency graph understandable? (can a new developer trace a request through the system?)

Change safety

• Is there comprehensive test coverage for critical paths? (> 70% line coverage, > 80% for critical modules)
• Can changes be deployed independently? (one team’s deployment does not require another team’s coordination)
• Is there a feature flag system for safe rollout of new functionality?
• Is the CI/CD pipeline fast enough to support rapid iteration? (< 15 minutes from commit to deployable artifact)

Knowledge distribution

• Are there at least 2 team members who can modify any critical component? (bus factor > 1)
• Are Architecture Decision Records (ADRs) maintained for significant decisions?
• Is the onboarding process documented? (can a new developer set up the development environment and make a change within 1 day?)
• Are system diagrams current? (updated within the last 6 months)

Red flag: If only one person can explain how a critical component works, the architecture has a dangerous knowledge silo.

Dimension 4: Performance

Does the architecture deliver acceptable response times and throughput under expected conditions?

Response time

• Are response time targets defined per endpoint/workflow? (e.g., API < 200ms p95, page load < 2s)
• Are the slowest endpoints identified and optimized? (top 10 by p99 latency)
• Are database queries optimized? (no full table scans, appropriate indexes, query plans reviewed)
• Is there effective use of caching? (frequently accessed, rarely changed data served from cache)

Resource efficiency

• Are resources appropriately sized? (not over-provisioned by 10x or under-provisioned and struggling)
• Are memory leaks monitored and addressed? (memory usage stable over days/weeks)
• Are database connections pooled and managed? (no connection exhaustion under load)
• Are background jobs isolated from request processing? (batch processing does not degrade API performance)

Observability

• Is distributed tracing implemented? (can you trace a single request across all services?)
• Are meaningful metrics collected? (business metrics, not just infrastructure metrics)
• Are alerts configured for performance degradation? (not just for outages)
• Are logs structured and searchable? (JSON format, correlation IDs, centralized log aggregation)

Red flag: If the answer to “what is the p99 latency?” is “I do not know,” the architecture lacks essential observability.

Dimension 5: Deployment

Can the team release changes safely, frequently, and with minimal manual effort?

CI/CD pipeline

• Is the deployment process fully automated? (no manual steps between commit and production)
• Is there a staging environment that mirrors production? (same infrastructure, similar data)
• Are database migrations automated and reversible?
• Can the team deploy multiple times per day without heroics?

Release safety

• Is there a rollback mechanism? (can the team revert to the previous version within minutes?)
• Are deployments zero-downtime? (no maintenance windows required for standard releases)
• Is there canary or blue-green deployment capability? (test new versions with a small percentage of traffic)
• Are deployment metrics monitored? (error rate, latency after deployment compared to before)

Infrastructure as Code

• Is infrastructure defined in code? (Terraform, Pulumi, CloudFormation — not manually configured)
• Can a new environment be provisioned from scratch automatically?
• Are infrastructure changes reviewed the same way as code changes? (PR-based workflow)
• Is there disaster recovery capability? (can the system be rebuilt in a different region?)

Red flag: If deployments require a “deployment lead” who manually executes steps from a runbook, the deployment architecture is fragile.

Dimension 6: Documentation

Is the architecture documented well enough for the team to operate, maintain, and extend the system?

Architecture documentation

• Do architecture diagrams exist at multiple levels of abstraction? (C4: context, container, component)
• Are diagrams current? (reflect the system as-built, not as-planned 2 years ago)
• Are key architectural decisions documented with rationale? (ADRs or equivalent)
• Are integration points documented? (APIs, message queues, shared databases, external services)

Operational documentation

• Are runbooks available for common operational tasks? (deployment, scaling, failover, data recovery)
• Is the incident response process documented? (who to call, how to diagnose, how to communicate)
• Are monitoring dashboards documented? (what each metric means, what thresholds are concerning)
• Are on-call procedures defined? (rotation, escalation, severity classification)

API documentation

• Are all APIs documented? (OpenAPI/Swagger for REST, schema documentation for GraphQL)
• Is API documentation generated from code? (not manually maintained — manual docs drift from reality)
• Are breaking changes communicated through a versioning strategy?
• Are API examples provided for common use cases?

Red flag: If the architecture exists only in one person’s head, the organization is one resignation away from losing its most critical knowledge.

Scoring and Reporting

After evaluating all dimensions, calculate the overall architecture health score:

Dimension	Weight	Score (1-5)	Weighted score
Scalability	20%	—	—
Security	20%	—	—
Maintainability	20%	—	—
Performance	15%	—	—
Deployment	15%	—	—
Documentation	10%	—	—
Overall	100%	—	—

Interpretation:

• 4.0-5.0: Strong architecture — focus on incremental improvements
• 3.0-3.9: Adequate — targeted investments needed in weak dimensions
• 2.0-2.9: Significant gaps — prioritize remediation before adding features
• 1.0-1.9: Critical — architecture is a blocker to business goals, major intervention required

How ARDURA Consulting Supports Architecture Reviews

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• 500+ senior specialists including software architects, principal engineers, and DevOps architects who have designed and reviewed systems at scale — available within 2 weeks
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• 99% client retention — architects who understand your business context and can provide actionable, not theoretical, recommendations
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Whether you need an independent architecture assessment before a major investment, a second opinion on a technology decision, or an architect to lead a remediation program, ARDURA Consulting provides the expertise to ensure your architecture supports your business ambitions.