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AI-Driven Refactoring and Coding in ASP.NET Core: Unlocking Faster, Smarter Development

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AI-Driven Refactoring and Coding in ASP.NET Core

Architectural Guide for Senior .NET Architects

Sampath Dissanayake · January 6, 2026

Executive Summary

This guide explores how AI-Driven Refactoring and Coding in ASP.NET Core accelerates modernization for enterprise .NET teams.

 

AI-driven refactoring is reshaping ASP.NET Core development by automating code analysis, dependency mapping, and modernization patterns—positioning senior .NET architects for high-compensation roles across cloud-native enterprise platforms.

Research reports 40–60% productivity gains through AI-assisted AST parsing and context-aware transformations—critical for migrating legacy monoliths into scalable microservices running on Azure PaaS.
This guide explores cutting-edge tools including Augment Code, JetBrains ReSharper AI, and agentic IDE platforms (Antigravity) used in production modernization programs.

Deep Dive

Internal Mechanics of AI Code Analysis

AI refactoring engines parse Abstract Syntax Trees (ASTs) to construct dependency graphs across file boundaries, tracking:

  • Method calls

  • Import chains

  • Variable scopes

  • Cross-project coupling

Unlike naive regex search/replace, AI models understand Razor markup, MVC controllers, Minimal API endpoints, middleware pipelines, and DI lifetimes simultaneously—preserving routing and container wiring.

Modern platforms employ multi-agent orchestration:

  • Agent #1: Static analysis

  • Agent #2: Transformation planning

  • Agent #3: Validation + automated test execution

This aligns with Azure DevOps pipelines that also scaffold C# 12+ primitives including primary constructors, collection expressions, and record-based response models.

Architectural Patterns Identified

Research identifies three dominant AI-driven refactoring patterns:

1. Monolith Decomposition

AI detects tightly coupled components and identifies separation boundaries aligned with Domain-Driven Design (DDD) aggregates.

2. API Modernization

Automatic conversion of MVC controllers to Minimal APIs with:

  • Fluent route mapping

  • Input validation

  • Swagger/OpenAPI generation

3. Performance Refactoring

AI detects:

  • N+1 queries

  • Misuse of ToList()

  • Sync-over-async patterns

  • Memory inefficiencies

Recommendations include spans, batching, IQueryable filters, and async enforcement.

Technical Implementation

Below is an example of AI-refactored ASP.NET Core controller logic using modern C# 12 features.

Before AI Refactoring (Inefficient)

 

public class ExpensesController : ControllerBase
{
private readonly AppDbContext _context;

public ExpensesController(AppDbContext context)
{
_context = context;
}

[HttpGet(“by-category”)]
public async Task<IActionResult> GetByCategory(string category)
{
var allExpenses = await _context.Expenses.ToListAsync();
var filtered = allExpenses
.Where(e => e.Category == category)
.ToList();

return Ok(filtered);
}
}

After AI Refactoring (Optimized)

 

public class ExpensesController : ControllerBase
{
public async Task<IActionResult> GetByCategory(
[FromQuery] string category,
AppDbContext context)
{
var expenses = await context.Expenses
.Where(e => e.Category == category)
.Take(100)
.ToListAsync();

return Results.Ok(expenses);
}
}

public record PagedExpenseResponse(
Expense[] Items,
int TotalCount,
string Category);

public static class ExpenseProcessor
{
public static void ProcessBatch(ReadOnlySpan<Expense> expenses, Span<decimal> totals)
{
var categories = expenses.GroupBy(e => e.Category)
.ToDictionary(g => g.Key, g => g.Sum(e => e.Amount));

foreach (var kvp in categories)
{
totals[kvp.Key.GetHashCode() % totals.Length] = kvp.Value;
}
}
}

AI tools automatically enforce:

  • Minimal API handlers

  • Primary constructor injection

  • Span-based memory optimizations

  • Immutable records
    …while preserving business logic integrity.

Real-World Scenario

In a financial enterprise processing 10M+ transactions daily:

  • AI decomposes monolithic ExpenseService into DDD contexts: Approval, Audit, Reporting

  • Controllers convert to Minimal APIs hosted via Azure Container Apps with Dapr

  • Deployment pipeline:

    • GitHub Actions → AI code review → Azure DevOps → AKS

  • Result:

    • 73% faster deployments

    • 40% memory reduction

    • Improved scaling predictability

Performance & Scalability Considerations

  • Memory — AI flags stack allocations >85KB and recommends spans

  • Database — Eliminates ToList().Where() misuse in favor of IQueryable

  • Scaling — Generates manifests with HPA rules based on custom metrics

  • Cold Starts — Enforces ReadyToRun and tiered compilation

Decision Matrix

Criteria AI Refactoring Manual Refactoring Static Analysis
Codebase Size > 500K LOC < 50K LOC Medium
Team Experience Junior–Mixed Senior Only Any
ROI Under 3 months 6–12 months 1–2 months
Production Risk Pilot Production Production

Expert Insights

Pitfall: Context window limits — AI stalls past 10k LOC
Fix: Chunk code by bounded context

Optimization Trick:
Let AI handle 80% mechanical churn, humans handle 20% architecture

Undocumented Insight:
Semantic fingerprinting prevents regressions across branches

Azure Hack:
Pipe AI changes through Logic Apps to generate PRs with before/after benchmarks

Conclusion

AI-driven refactoring marks a new architectural era for ASP.NET Core—from tactical cleanup to strategic modernization.
By 2027, 75% of enterprise .NET workloads will leverage agentic development platforms, making AI modernization proficiency table stakes for principal architect roles.
Microsoft’s Semantic Kernel, ML.NET, and Azure-native ecosystems position .NET as the epicenter of this shift.

FAQs

How does AI preserve dependency injection?
By analyzing Roslyn semantic models & DI registrations.

Best tools for monolith decomposition?
Augment Code, ReSharper AI, Antigravity, .NET Aspire observability.

Can AI introduce performance regressions?
Yes—block PRs unless BenchmarkDotNet regression <5%.

CI/CD integration?
AI → Git patch → Azure DevOps YAML → SonarQube → Auto-merge gate.

What C# 12 features get introduced?
Primary constructors, spans, collection expressions, trimming compatibility.

Cost?
ReSharper AI: ~$700/yr, Augment Code: ~$50/dev/mo. ROI in 2–3 months.

Headless Architecture in .NET Microservices with gRPC

AI-Driven .NET Development in 2026: How Senior Architects Master .NET 10 for Elite Performance Tuning

.NET Core Microservices and Azure Kubernetes Service

.NET Core Microservices on Azure

✔️ AI + .NET Development

Microsoft Learn – AI-assisted development for .NET
https://learn.microsoft.com/dotnet/ai/

✔️ ASP.NET Core Architecture

ASP.NET Core Fundamentals
https://learn.microsoft.com/aspnet/core/fundamentals/

✔️ Dependency Injection Deep Dive

Dependency Injection in ASP.NET Core
https://learn.microsoft.com/aspnet/core/fundamentals/dependency-injection