Review Software Architecture

Evaluate software architecture at the system level for quality attributes, design principles adherence, and long-term maintainability.

When to Use

• Evaluating a proposed architecture before implementation begins
• Assessing an existing system for scalability, maintainability, or security
• Reviewing Architecture Decision Records (ADRs) for a project
• Performing a technical debt assessment
• Evaluating whether a system is ready for a significant scale-up or feature expansion
• Differentiating from line-level code review (which focuses on PR-level changes)

Inputs

• Required: System codebase or architecture documentation (diagrams, ADRs, README)
• Required: Context about the system's purpose, scale, and constraints
• Optional: Non-functional requirements (latency, throughput, availability targets)
• Optional: Team size and skill composition
• Optional: Technology constraints or preferences
• Optional: Known pain points or areas of concern

Procedure

Step 1: Understand the System Context

Map the system boundaries and interfaces:

## System Context
- **Name**: [System name]
- **Purpose**: [One-line description]
- **Users**: [Who uses it and how]
- **Scale**: [Requests/sec, data volume, user count]
- **Age**: [Years in production, major versions]
- **Team**: [Size, composition]

## External Dependencies
| Dependency | Type | Criticality | Notes |
|-----------|------|-------------|-------|
| PostgreSQL | Database | Critical | Primary data store |
| Redis | Cache | High | Session store + caching |
| Stripe | External API | Critical | Payment processing |
| S3 | Object storage | High | File uploads |

Expected: Clear picture of what the system does and what it depends on. On failure: If architecture documentation is missing, derive the context from code structure, configs, and deployment files.

Step 2: Evaluate Structural Quality

Coupling Assessment

Examine how tightly modules depend on each other:

• Dependency direction: Do dependencies flow in one direction (layered) or circular?
• Interface boundaries: Are modules connected through defined interfaces/contracts or direct implementation references?
• Shared state: Is mutable state shared between modules?
• Database coupling: Do multiple services read/write the same tables directly?
• Temporal coupling: Must operations happen in a specific order without explicit orchestration?

# Detect circular dependencies (JavaScript/TypeScript)
npx madge --circular src/

# Detect import patterns (Python)
# Look for deep cross-package imports
grep -r "from app\." --include="*.py" | sort | uniq -c | sort -rn | head -20

Cohesion Assessment

Evaluate whether each module has a single, clear responsibility:

• Module naming: Does the name accurately describe what the module does?
• File size: Are files or classes excessively large (>500 lines suggests multiple responsibilities)?
• Change frequency: Do unrelated features require changes to the same module?
• God objects: Are there classes/modules that everything depends on?

Coupling Level	Description	Example
Low (good)	Modules communicate through interfaces	Service A calls Service B's API
Medium	Modules share data structures	Shared DTO/model library
High (concern)	Modules reference each other's internals	Direct database access across modules
Pathological	Modules modify each other's internal state	Global mutable state

Expected: Coupling and cohesion assessed with specific examples from the codebase. On failure: If the codebase is too large for manual review, sample 3-5 key modules and the most-changed files.

Step 3: Assess SOLID Principles

Principle	Question	Red Flags
Single Responsibility	Does each class/module have one reason to change?	Classes with >5 public methods on unrelated concerns
Open/Closed	Can behavior be extended without modifying existing code?	Frequent modifications to core classes for each new feature
Liskov Substitution	Can subtypes replace their base types without breaking behavior?	Type checks (instanceof) scattered through consumer code
Interface Segregation	Are interfaces focused and minimal?	"Fat" interfaces where consumers implement unused methods
Dependency Inversion	Do high-level modules depend on abstractions, not details?	Direct instantiation of infrastructure classes in business logic

## SOLID Assessment
| Principle | Status | Evidence | Impact |
|-----------|--------|----------|--------|
| SRP | Concern | UserService handles auth, profile, notifications, and billing | High — changes to billing risk breaking auth |
| OCP | Good | Plugin system for payment providers | Low |
| LSP | Good | No type-checking anti-patterns found | Low |
| ISP | Concern | IRepository has 15 methods, most implementors use 3-4 | Medium |
| DIP | Concern | Controllers directly instantiate database repositories | Medium |

Expected: Each principle assessed with at least one specific example. On failure: Not all principles apply equally to every architecture style. Note when a principle is less relevant (e.g., ISP matters less in functional codebases).

Step 4: Review API Design

For systems that expose APIs (REST, GraphQL, gRPC):

• Consistency: Naming conventions, error formats, pagination patterns uniform
• Versioning: Strategy exists and is applied (URL, header, content negotiation)
• Error handling: Error responses are structured, consistent, and don't leak internals
• Authentication/Authorization: Properly enforced at the API layer
• Rate limiting: Protection against abuse
• Documentation: OpenAPI/Swagger, GraphQL schema, or protobuf definitions maintained
• Idempotency: Mutating operations (POST/PUT) handle retries safely

## API Design Review
| Aspect | Status | Notes |
|--------|--------|-------|
| Naming consistency | Good | RESTful resource naming throughout |
| Versioning | Concern | No versioning strategy — breaking changes affect all clients |
| Error format | Good | RFC 7807 Problem Details used consistently |
| Auth | Good | JWT with role-based scopes |
| Rate limiting | Missing | No rate limiting on any endpoint |
| Documentation | Concern | OpenAPI spec exists but 6 months out of date |

Expected: API design reviewed against common standards with specific findings. On failure: If no API is exposed, skip this step and focus on internal module interfaces.

Step 5: Evaluate Scalability and Reliability

• Statelessness: Can the application scale horizontally (no local state)?
• Database scalability: Are queries indexed? Is the schema suitable for the data volume?
• Caching strategy: Is caching applied at appropriate layers (database, application, CDN)?
• Failure handling: What happens when a dependency is unavailable (circuit breaker, retry, fallback)?
• Observability: Are logs, metrics, and traces implemented?
• Data consistency: Is eventual consistency acceptable or is strong consistency required?

Expected: Scalability and reliability assessed relative to stated non-functional requirements. On failure: If non-functional requirements are undocumented, recommend defining them as a first step.

Step 6: Assess Technical Debt

## Technical Debt Inventory
| Item | Severity | Impact | Estimated Effort | Recommendation |
|------|----------|--------|-----------------|----------------|
| No database migrations | High | Schema changes are manual and error-prone | 1 sprint | Adopt Alembic/Flyway |
| Monolithic test suite | Medium | Tests take 45 min, developers skip them | 2 sprints | Split into unit/integration/e2e |
| Hardcoded config values | Medium | Environment-specific values in source code | 1 sprint | Extract to env vars/config service |
| No CI/CD pipeline | High | Manual deployment prone to errors | 1 sprint | Set up GitHub Actions |

Expected: Technical debt catalogued with severity, impact, and effort estimates. On failure: If the debt inventory is overwhelming, prioritize the top 5 items by impact/effort ratio.

Step 7: Review Architecture Decision Records (ADRs)

If ADRs exist, evaluate:

• Decisions have clear context (what problem was being solved)
• Alternatives were considered and documented
• Trade-offs are explicit
• Decisions are still current (not superseded without documentation)
• New significant decisions have ADRs

If ADRs don't exist, recommend establishing them for key decisions.

Step 8: Write the Architecture Review

## Architecture Review Report

### Executive Summary
[2-3 sentences: overall health, key concerns, recommended actions]

### Strengths
1. [Specific architectural strength with evidence]
2. ...

### Concerns (by severity)

#### Critical
1. **[Title]**: [Description, impact, recommendation]

#### Major
1. **[Title]**: [Description, impact, recommendation]

#### Minor
1. **[Title]**: [Description, recommendation]

### Technical Debt Summary
[Top 5 debt items with prioritized recommendations]

### Recommended Next Steps
1. [Actionable recommendation with clear scope]
2. ...

Expected: Review report is actionable with prioritized recommendations. On failure: If the review is time-boxed, clearly state what was covered and what remains unassessed.

Validation

• System context documented (purpose, scale, dependencies, team)
• Coupling and cohesion assessed with specific code examples
• SOLID principles evaluated where applicable
• API design reviewed (if applicable)
• Scalability and reliability assessed against requirements
• Technical debt catalogued and prioritized
• ADRs reviewed or their absence noted
• Recommendations are specific, prioritized, and actionable

Common Pitfalls

• Reviewing code instead of architecture: This skill is about system-level design, not line-level code quality. Use code-reviewer for PR-level feedback.
• Prescribing a specific technology: Architecture reviews should identify problems, not mandate specific tools unless there's a clear technical reason.
• Ignoring team context: The "best" architecture for a 3-person team differs from a 30-person team. Consider organizational constraints.
• Perfectionism: Every system has tech debt. Focus on debt that is actively causing pain or blocking future work.
• Assuming scale: Don't recommend distributed systems for an app serving 100 users. Match architecture to actual requirements.

Related Skills

• security-audit-codebase — security-focused code and configuration review
• configure-git-repository — repository structure and conventions
• design-serialization-schema — data schema design and evolution
• review-data-analysis — review of analytical correctness (complementary perspective)