AI-Assisted Refactoring: Modernising Legacy Codebases at Scale

Legacy code is a silent tax on every engineering team. It accumulates over years — layers of undocumented decisions, deprecated frameworks, and tightly coupled modules that nobody dares touch. For most enterprises, the codebase is simultaneously their greatest asset and their biggest liability. Modernising it has traditionally meant multi-year rewrites, massive disruption, and significant risk. Until now.

AI-assisted refactoring is fundamentally changing the economics of legacy modernisation. What once required a dedicated team working for 18 months can now be approached incrementally, intelligently, and at a fraction of the cost. Engineering leaders at organisations like Kmart and Air Liquide have already seen what's possible — development cycles cut by up to 80% when AI is embedded into the modernisation workflow.

This post breaks down how AI refactoring tools work, what they're genuinely good at, and how your team can start applying them to your most stubborn legacy systems today.

Why Legacy Codebases Are So Hard to Modernise

Before exploring the solution, it's worth understanding the problem clearly. Legacy systems are difficult to refactor for several compounding reasons:

  • Missing context: The original developers are often gone, and institutional knowledge lives in people's heads — not in comments or documentation.
  • Tight coupling: Business logic is tangled with infrastructure concerns, making isolated changes risky.
  • Inadequate test coverage: Legacy systems frequently have low unit test coverage, meaning changes can't be validated automatically.
  • Dependency sprawl: Outdated libraries with no clear migration path, compounding security vulnerabilities.

Traditional refactoring requires engineers to hold enormous amounts of context in their heads simultaneously. AI changes this entirely — because LLMs can ingest and reason about thousands of lines of code at once, finding patterns and relationships that no single human would spot in a reasonable timeframe.

How AI Understands Your Codebase

Modern AI refactoring tools don't just apply syntactic transformations — they build a semantic model of your codebase using a combination of embeddings, RAG (Retrieval-Augmented Generation), and large context windows.

Here's how it works in practice:

  1. Embedding generation: Each file, function, and module is converted into a vector embedding that captures its semantic meaning — not just its syntax.
  2. Graph-based indexing: Call graphs, dependency trees, and data flow are mapped so the AI understands how components relate to each other.
  3. RAG retrieval: When you ask the AI to refactor a module, it retrieves relevant context — related functions, interface definitions, test cases — before generating output.
  4. Targeted generation: With full context, the AI produces refactored code that respects existing interfaces, naming conventions, and architectural patterns.

Tools like GitHub Copilot Workspace, Cursor, and Aider all leverage variations of this approach. For enterprise-grade codebases, Infonex builds bespoke codebase-aware AI pipelines that go further — indexing documentation, ADRs (Architecture Decision Records), and Jira tickets alongside the source code itself.

What AI Refactoring Actually Looks Like

Let's make this concrete. Consider a common legacy pattern: a monolithic service class that violates the Single Responsibility Principle and mixes database access with business logic.

// Legacy: 400-line OrderService with mixed concerns
public class OrderService {
    private readonly SqlConnection _conn;

    public Order CreateOrder(int customerId, List<CartItem> items) {
        // Validate customer (embedded SQL)
        var cmd = new SqlCommand(
            $"SELECT * FROM Customers WHERE Id = {customerId}", _conn);
        var reader = cmd.ExecuteReader();
        if (!reader.HasRows) throw new Exception("Customer not found");

        // Calculate total (business logic buried in data access)
        decimal total = 0;
        foreach (var item in items) {
            total += item.Price * item.Quantity;
        }

        // Insert order (more raw SQL)
        var insertCmd = new SqlCommand(
            $"INSERT INTO Orders VALUES ({customerId}, {total}, GETDATE())", _conn);
        insertCmd.ExecuteNonQuery();

        return new Order { CustomerId = customerId, Total = total };
    }
}

An AI refactoring agent given this file — along with context about the broader codebase — will identify the violations, propose a split into IOrderRepository, ICustomerRepository, and a clean OrderService, generate parameterised queries to eliminate SQL injection risks, and produce corresponding unit tests. A task that might take a senior engineer a full day can be drafted in minutes, ready for human review.

The key insight: AI handles the mechanical transformation; engineers handle the judgement calls. This is the right division of labour.

Modernising at Scale: Patterns That Work

AI refactoring works best when applied systematically rather than ad hoc. Here are the patterns that consistently deliver results for enterprise teams:

Strangler Fig with AI Acceleration

The strangler fig pattern — incrementally replacing legacy functionality behind a stable interface — is supercharged by AI. Tools can automatically generate the adapter layer, write the new implementation, and generate equivalence tests that confirm the new module behaves identically to the old one. What used to take weeks per module can be compressed to days.

Automated Test Generation Before Refactoring

One of the highest-leverage uses of AI in legacy modernisation is generating a characterisation test suite before touching any production code. Tools like CodiumAI and Diffblue Cover can achieve 80%+ line coverage on legacy Java and C# codebases automatically, creating a safety net that makes subsequent refactoring far less risky.

Dependency Upgrade Pipelines

AI agents can scan your package.json, pom.xml, or requirements.txt, identify outdated dependencies, check for breaking changes in changelogs, and generate migration diffs — all automatically. GitHub's Dependabot does the detection; AI takes it further by writing the migration code itself.

Documentation Generation in Parallel

One of the most undervalued outputs of AI-assisted refactoring is documentation. As the AI processes each module, it can generate inline comments, update README files, and produce architectural diagrams — ensuring the next engineer doesn't face the same knowledge vacuum that made the legacy code so difficult to work with in the first place.

Measuring the Impact

The numbers from early enterprise adopters are striking. A McKinsey Global Survey (2023) found that organisations using AI in software development reported a 20–45% reduction in time spent on refactoring tasks. GitHub's own research on Copilot found developers completed tasks 55% faster on average.

At Infonex, our enterprise engagements consistently exceed these benchmarks when AI is embedded throughout the full modernisation workflow — not just at the code generation step. Clients like Kmart and Air Liquide have validated 80% faster development cycles when AI is applied systematically across planning, implementation, testing, and documentation.

The critical differentiator isn't the AI tool itself — it's the workflow architecture around it. Most teams underutilise AI by treating it as an autocomplete engine. When you treat it as a first-class engineering collaborator with full codebase context, the productivity gains compound dramatically.

Where to Start: A Practical Roadmap

If you're an engineering leader looking to apply AI refactoring to your legacy estate, here's a pragmatic starting point:

  1. Identify your highest-friction module. Pick one service or module that causes the most pain — slow to change, frequently buggy, poorly documented. Start there.
  2. Generate characterisation tests first. Before any refactoring, use AI to build your safety net. This step alone has enormous value.
  3. Use AI to propose the refactored architecture. Don't write the design doc yourself — let AI draft it based on the codebase, then review and refine with your team.
  4. Implement incrementally with AI pair programming. Use Copilot Workspace, Cursor, or a bespoke pipeline to generate each module in the new architecture.
  5. Measure and iterate. Track cycle time, bug rate, and developer satisfaction. The data will make the case for scaling the approach.

Conclusion

AI-assisted refactoring isn't a silver bullet — but it is a genuine force multiplier for engineering teams tackling legacy modernisation. The combination of large context windows, semantic code understanding, and intelligent generation means that the most tedious, high-risk parts of modernisation work can now be accelerated dramatically.

For engineering leaders, the strategic question is no longer whether to use AI in your modernisation programme — it's how quickly you can build the workflows that unlock its full potential. Every month you delay is a month your competitors are compounding their AI advantage.

The legacy codebase you've been putting off? It's finally time.

Accelerate Your Legacy Modernisation with Infonex

Infonex is Australia's specialist AI consultancy for enterprise engineering teams. We bring deep expertise in AI-accelerated development, RAG pipelines, and codebase-aware AI workflows — and we offer a free consulting session to help you map out your modernisation roadmap.

Our clients — including Kmart and Air Liquide — have achieved 80% faster development cycles by embedding AI throughout their engineering workflows, not just at the edges.

📅 Book your free AI consulting session at infonex.com.au

Whether you're planning a legacy migration, evaluating AI tooling, or building your first RAG pipeline — we'll help you cut through the noise and get to value fast.

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