Introduction
Traditional code reviews are manual, inconsistent, and heavily depend on individual reviewer expertise. As projects scale — especially enterprise Angular front-end + .NET backend ecosystems — enforcing architectural rules, security standards, and naming conventions becomes increasingly difficult.
An Intelligent Code Review Engine solves this bottleneck by automatically analyzing code changes, detecting violations, predicting risk level, recommending fixes, and optionally applying auto-corrections.
This article walks through how to build such a system, from static analysis and AST-based rule engines to machine learning–based smell detection and CI integration.
Core goals
Enforce coding standards (naming, layering, import patterns, async/sync usage).
Detect architectural violations (feature boundaries, circular dependencies, incorrect service calls).
Identify anti-patterns (God components, duplicate code, deeply nested conditions, unused injections).
Provide suggested fixes—ideally auto-generated.
Integrate with CI to block risky merges or require approval.
High-level architecture
Developer IDE → Local Pre-Check (Plugin)
|
▼
Intelligent Review Engine
(Rule Engine + ML Model)
|
┌──────────────┴──────────────┐
│ Suggested Fixes │
│ Risk Score + Report │
└──────────────┬──────────────┘
|
CI/Git Hook
|
▼
Merge Allowed / Blocked
Main components
| Component | Responsibility |
|---|
| Parser layer | Converts source code to AST and dependency graphs |
| Static rule engine | Declarative rule checks (e.g., imports, naming, DI structure) |
| ML-based smell detection | Predicts maintainability, duplication, complexity, code smell probability |
| Auto-fixer | Suggests edits or generates patches |
| CI integration | Enforces thresholds |
Parsing and foundations
Angular parsing
Use
Capture
Component metadata
Module structure and imports
Routing hierarchy
Dependency injection metadata
Template bindings (parse via Angular compiler AST)
.NET parsing
Use Roslyn
Capture
Rule types
1. Syntax-level rules (low complexity)
Examples
File naming conventions
Folder structure enforcement
Enum naming: PascalCase
Private fields: _camelCase
No inline anonymous functions longer than N lines
Output
❌ File "userService.ts" breaks naming rule. Expected: "user.service.ts"
2. Dependency and architecture rules
Examples
Angular component cannot directly call HttpClient if a domain service exists
Shared module cannot depend on feature modules
No repository call allowed inside controllers—must go through service layer
No circular dependency:
ERROR: user.module ↔ profile.module circular reference detected.
Graph approach
3. Pattern and anti-pattern detection
Large component (>300 lines) → suggest splitting
Service with >8 injected dependencies → smells like God Service
Too deep nesting → recommend refactor
Async calls without try/catch → mark as unsafe
Severity scored by risk model.
4. Security rules
Hardcoded secrets, tokens, endpoints
Unsafe query building (SQL injection risk)
Angular DomSanitizer misuse or missing sanitization
Authentication bypass in controller endpoint
JWT or cookie misconfiguration
ML-based smell detection
Instead of only rule matching, use ML to detect patterns similar to technical debt.
Techniques
| Type | Method |
|---|
| NLP on code | CodeBERT, GPT embeddings, TF-IDF |
| Metrics analysis | Cyclomatic complexity, LCOM, fan-in/out dependencies |
| Clustering | Find outlier modules (high complexity + high churn) |
| Prediction | Train logistic regression or random forest: "smelly vs. clean" |
Source data
Auto-fixing system
Categories
Safe autofix: rename symbol, re-order imports, enforce consistent formatting
Semi-automatic patches: refactor method, extract component, wrap async with try/catch
Human-approved suggestions: re-architect layering violation or circular dependency resolution
Example generated patch
- export class UserService {+ @Injectable({ providedIn: 'root' })+ export class UserService {
or for .NET:
- new SqlConnection(connectionString)+ new SqlConnection(_config.GetConnectionString("Default"))
Risk scoring engine
Score each violation using:
severity = (impact × confidence × frequency) – mitigation score
Dimensions
Impact (performance, maintainability, security)
Confidence (rule-based = high, ML heuristic = medium)
Fixability (can auto-fix or requires manual refactor)
Threshold examples
| Severity | Action |
|---|
| 0–20 | Warn |
| 21–60 | Require approval |
| >60 | Block merge |
CI/CD integration
Steps in GitHub Actions or Azure DevOps:
Run → Parse → Evaluate → Score → Generate report → Decide (allow/deny)
Artifacts exported
Developer workflow
IDE (VS Code + Rider) gets
PR review stage
⚠ 13 Suggestions
❌ 2 Must-Fix Violations
Risk Score: 78 (Merge Blocked)
Metrics and monitoring
Track
Number of prevented architectural violations
Reduction in defects in hot modules
Merge latency before/after adoption
Auto-fixed suggestions ratio
Developer acceptance rate of AI fixes
Challenges and mitigations
| Challenge | Mitigation |
|---|
| False positives annoy devs | Feedback loop + suppression with justification |
| Slow analysis on big repos | Incremental diff-based scanning |
| ML drift as coding style evolves | Continuous retraining |
| Developers bypass rules | CI enforcement + governance policies |
Roadmap (Optional Enhancements)
Natural language justification: “Why is this wrong?”
Refactoring as code action UI (visual tree mapping)
Baseline comparison across tenants or projects
Training mode: learn project conventions dynamically
Summary
An Intelligent Code Review Engine unlocks:
Faster PR cycles
Enforced architecture consistency
Lower risk of long-term technical debt
Better developer experience with automated fixes
A scalable code governance system across Angular and .NET ecosystems
By combining static rules, architecture graphs, ML models, and CI enforcement, this platform acts as a continuous architecture guardian, not just a lint tool.