How to Safely Add New Features to Legacy Code — A Developer’s Guide

Legacy code doesn’t have to be a nightmare.

At Simplico Co., Ltd., we regularly help clients improve, stabilize, and extend legacy systems that are old, undocumented, and fragile. One of the most common (and risky) tasks? Adding new features.

This guide shares our proven, step-by-step workflow for working with legacy Python code, and how to add functionality safely — without rewriting everything.

🚧 What Is Legacy Code?

Legacy code is not just “old code.” It’s any code that is:

❌ Difficult to understand
❌ Hard to test
❌ Risky to change
❌ Missing documentation
❌ Tightly coupled to outdated technologies

And yet... it still works. And it still runs your business.

🔁 Our Workflow: How to Add Features Safely

Adding a new feature to legacy software can feel like defusing a bomb. Here’s the process we use to do it safely, repeatably, and without breaking production.

🗺️ Mermaid.js Visual Workflow

flowchart TD
    A["🧭 Understand Existing Code"]
    B["🧪 Write Characterization Tests"]
    C["🧹 Refactor the Relevant Area"]
    D["🌱 Add the New Feature"]
    E["🔄 Test, Review, and Deploy"]
    F["🧽 Leave the Code Better Than You Found It"]

    A --> B --> C --> D --> E --> F

1️⃣ Understand What You’re Working With

Before you write a single line of code:

Trace the flow of logic.
Read related functions and classes.
Look at production logs or database activity.
Talk to users or business stakeholders.

Goal: Minimize surprises. Know what you're touching.

2️⃣ Write Characterization Tests

Legacy code usually has no tests. Before changing anything, write characterization tests — tests that capture how the code behaves today.

def test_convert_date_format():
    assert convert_date("2024-01-01") == "01-Jan-2024"

Use tools like:

pytest
coverage.py
unittest.mock or pytest-mock

Goal: Prevent accidental regressions.

3️⃣ Refactor (Just a Little)

Don’t rewrite everything. Just improve the part you’re about to change:

Extract long methods
Rename confusing variables
Break up tightly coupled logic
Inject dependencies for testability

Tools we use:

black, isort, rope, flake8, pylint

Goal: Make the code easier to work with — without changing its behavior.

4️⃣ Add the Feature

Now that it's tested and cleaned up, add your new functionality.

Use feature flags if the change is risky or needs staged rollout:

if settings.ENABLE_NEW_REPORT:
    return new_report_logic()
else:
    return old_logic()

Goal: Deliver new value while keeping everything else stable.

5️⃣ Test, Review, Deploy

Rerun all tests
Code review with teammates
Stage your feature with real data
Monitor logs after deployment

Goal: Safely ship your feature with confidence.

6️⃣ Leave the Code Better Than You Found It

Before closing your pull request:

Add comments or docstrings
Keep tests
Delete dead code if safe
Log weird behaviors for the future

This is how legacy code improves — one change at a time.

🧰 Recommended Tools for Python Legacy Code

Purpose	Tools
Testing	`pytest`, `coverage.py`, `hypothesis`
Static Analysis	`flake8`, `pylint`, `bandit`, `radon`, `vulture`
Refactoring	`rope`, `bowler`, `fissix`
Code Formatting	`black`, `isort`
Type Checking	`mypy`, `pyannotate`
Docs	`pdoc`, `Sphinx`
CI/CD & Automation	`tox`, `GitHub Actions`, `Jenkins`

💬 Final Thoughts

Adding features to legacy systems isn’t glamorous, but it’s real engineering. The business depends on these systems, and improving them safely is a skill in high demand.

Work patiently. Test thoroughly. Refactor gradually. And always leave the code a little better than you found it.