The Analog Refactor Studio: Designing a Paper Walkthrough Before You Touch Legacy Code

Legacy C/C++ systems are rarely small, clean, or well-documented. They’re messy, mission-critical, and often too big for anyone to fully understand. Yet, they still need to evolve.

If you’ve ever opened a 20-year-old codebase and thought, “There is no way I can safely refactor this”, you’re not alone. The key is recognizing that you don’t refactor a monolith in one heroic leap. You design your moves—carefully, on paper—before you touch a single line of production code.

This is the idea behind the Analog Refactor Studio: a deliberate, low-tech, high-impact way to design and validate large refactors using paper walkthroughs, test scaffolding, and atomic changes.

Why You Can’t “One-Shot” a Giant Legacy Refactor

Large C/C++ codebases at companies like ASML or Atlassian typically have:

• Hundreds or thousands of modules
• Intertwined dependencies built up over decades
• Scarce or outdated documentation
• People who used to understand the system but left years ago

Attempting a single grand refactor that “fixes everything” is a recipe for:

• Unbounded scope creep – you keep discovering more dependencies and edge cases
• Long-lived branches – painful to keep up to date and risky to merge
• Broken production – subtle behavior changes that tests (if any exist) don’t catch

Instead, you need to:

1. Prioritize where to start. Which parts of the system deliver the most benefit if improved—performance, maintainability, correctness?
2. Define crisp refactor boundaries. Refactor this module, not the entire architecture.
3. Plan the steps before coding. That’s where the Analog Refactor Studio comes in.

What Is an Analog Refactor Studio?

An Analog Refactor Studio is a structured, paper-first session where you:

• Map out the code involved in a refactor
• Sketch refactoring steps and intermediate states
• Identify hazards (ABI changes, breaking interfaces, circular dependencies)
• Define how you’ll validate success with tests

You do this before you open your IDE.

The goal is to make the refactor boringly predictable. By the time you write code, you already know:

• Which files you’ll touch
• In what order
• How you’ll keep the system buildable and testable at each step

This is particularly effective in large C/C++ systems, where build times, link-time dependencies, and binary compatibility all matter.

Step 1: Choose the Right Starting Point

You can’t refactor everything. Start with areas that are:

• High leverage – A module used everywhere (e.g., logging, configuration, math utilities) where cleanups propagate value broadly.
• High pain – Bug-prone or hard-to-maintain areas blocking new features.
• Stable behaviorally – Logic that doesn’t change functionally often, so you can focus on structural improvement.

Ask:

• Which components are change hotspots?
• Where do developers routinely complain or get stuck?
• What part, if improved, would measurably speed up development or reduce defects?

This is what teams working on large platforms at Atlassian and ASML have found over and over: local, high-leverage refactors win.

Step 2: Build and Harden Your Test Safety Net

Refactoring means “change structure without changing behavior.” That last part—without changing behavior—only has meaning if you can measure behavior.

Before refactoring, you want:

1. Automatic unit tests for the logic you’re about to change.
2. Integration or system tests for critical workflows.
3. Fast feedback – tests should run reliably and often.

In legacy C/C++ code, this might involve:

• Extracting complex functions into testable units
• Adding test harnesses around old libraries
• Using abstraction layers to stub external dependencies (hardware, network, filesystem)

The more tests you have before refactoring, the more aggressively you can change the structure while keeping behavior stable.

Step 3: Run an Analog Refactor Session (Paper Walkthrough)

Now, create your Analog Refactor Studio. This can be a whiteboard, index cards, a notebook, or a shared diagramming tool used as if it were paper.

3.1 Map the Current State

On paper, capture:

• Key structs/classes and their main responsibilities
• The most important functions and APIs
• Direct dependencies between components

Don’t try to diagram the whole codebase. Draw just enough to:

• Understand how data flows
• See where dependencies tangle
• Identify the seams where you can safely cut

3.2 Design the Target State

Next, sketch your target design:

• Which responsibilities move where?
• What new interfaces or abstractions will exist?
• Which dependencies are removed or inverted?

For example, you might:

• Extract a file-format parser from a monolithic module into its own library
• Replace raw pointers and manual memory management with RAII wrappers
• Introduce an interface so multiple backends can be swapped at runtime

The sketch does not need to be production-quality UML. Boxes and arrows are fine.

3.3 Plan the Intermediate Steps

Most big refactors fail because they only define a before and after state and skip the journey in between.

On paper, define a sequence of small, coherent steps:

1. Introduce a new interface side-by-side with the old one
2. Migrate one caller at a time
3. Remove the old interface once all callers are moved

Each step should:

• Compile
• Run tests
• Keep behavior unchanged

This gives you a series of checkpoints you can commit and verify.

3.4 Identify Risk and Validation

For each step, write down:

• What could go wrong? (e.g., ABI breaks, subtle order-of-initialization changes, timing issues)
• Which tests catch it? (unit, integration, fuzzing, performance tests)

When teams at ASML, for example, refactor performance-critical components, they typically pair structural refactors with performance regression tests to ensure nothing slow creeps in.

Step 4: Execute as Atomic, Tested Refactor Commits

Once the paper walkthrough is stable, you implement it in code, usually as:

• One logically atomic refactor per commit (or small set of commits)
• Each commit guarded by passing tests

An atomic refactor commit aims to:

• Touch many files if necessary
• Change only structure, not behavior
• Keep the system buildable and releasable

Why this works:

• Reviewers see a consistent, cohesive change
• You avoid “half-migrated” states sitting in main
• If something breaks, git bisect can isolate the culprit quickly

For very large changes, you can:

• Break into multiple atomic steps, each keeping the system stable
• Use feature flags or dual-API periods if changing public interfaces

The combination of paper design + atomic commits + tests can keep massive, old systems remarkably stable, even as many files are touched in one go.

Step 5: Handling Cross-Project Refactors Safely

Some refactors cut across multiple repositories or services—common in large organizations.

A safe cross-project strategy usually looks like this:

1. Add the new API to the shared library, keeping the old one.
2. Update all consumers (possibly across many repos) to use the new API.
3. Remove the old API only when all consumers have migrated.

Coordinating this can be done by:

• Using versioned packages or shared library releases
• Communicating a clear deprecation plan and timeline
• Automating consumer updates when possible

This preserves atomicity at the system level: any given consumer is always compatible with some version of the shared library, and your CI pipelines enforce that compatibility.

Step 6: Bringing AI-Assisted Tools into the Studio

Modern AI-assisted tools, such as Rovo Dev and others, can be powerful companions for your Analog Refactor Studio, especially in very large codebases.

You can use AI to:

• Navigate and summarize unfamiliar parts of the codebase quickly
• Propose refactoring steps given your target design and constraints
• Generate mechanical edits (e.g., renames, signature changes, wrapper insertions) across hundreds of files
• Draft tests based on observed usage and existing code

The key is to treat the AI as a power tool, not an autopilot:

• You define the plan in your analog refactor session
• AI tools help execute repetitive steps and suggest patterns
• Your tests and reviews enforce correctness and intent

Teams working on large systems at Atlassian, ASML, and similar organizations have found that combining:

• Careful upfront design
• Strong test coverage
• Automation and AI-assisted refactors

makes previously unthinkable, large-scale legacy refactors feasible.

Putting It All Together

Designing big refactors in large legacy C/C++ systems is less about heroic coding and more about methodical engineering.

The Analog Refactor Studio gives you a practical sequence:

1. Prioritize a high-impact, bounded refactor target.
2. Build a test safety net around the behavior you must preserve.
3. Map current and target designs on paper, not in your head.
4. Plan intermediate, buildable steps with known risks and validations.
5. Execute as atomic, tested commits, keeping main always stable.
6. Coordinate cross-project changes with dual APIs and versioning.
7. Use AI tools to accelerate mechanical work, while you own the design.

Before you next dive into that intimidating legacy codebase, resist the urge to “just start refactoring.” Instead, open a notebook, step into your Analog Refactor Studio, and design the walkthrough first.

You’ll move slower at the beginning—but much faster, safer, and more confidently overall.