What makes legacy code hard to maintain

Legacy codebases come with their own set of obstacles, and they tend to slow down even experienced developers. The challenges typically fall into five categories:

  • Missing expertise in older technologies: Languages like COBOL, Fortran, and older C++ variants still power critical infrastructure, but fewer developers have hands-on experience with them.
  • Absent or outdated documentation: Code written under tight deadlines rarely gets proper docs. When the original developers move on, they take crucial context with them, and subsequent patches often go undocumented.
  • “Spaghetti code”: Years of rushed fixes and feature additions turn once-readable logic into deeply nested, nonlinear structures that are hard to follow and risky to modify.
  • Outdated practices: Legacy code reflects the frameworks, libraries, and architectural conventions of its era, which are often incompatible with modern systems and standards.
  • Fear of breaking things: Legacy systems frequently lack test coverage, so even small changes can trigger unexpected ripple effects.

A common saying holds that clean code doesn’t need comments. But code no one can understand isn’t clean in any practical sense — and a few well-placed comments explaining the why behind the what can make all the difference for the next developer. Comments also provide additional context that helps GitHub Copilot generate better, more relevant suggestions.

Where Copilot fits in legacy work

GitHub Copilot is often discussed as a tool for writing new code, but it’s just as useful for making sense of existing code. By analyzing the code you’re working on, it can generate explanations and documentation for complex logic, cryptic functions, and hidden dependencies. Four areas stand out:

  1. Explaining what code does: If you’re a Python developer staring at an unfamiliar COBOL or C++ function, you can prompt Copilot with something like “Explain this code to me like I’m a Python developer.” This bridges knowledge gaps without hours of manual research.
  2. Translating between language eras: Copilot can help convert older syntax into modern equivalents. Think of it like machine translation for code — it handles the raw conversion but may need human input for context, nuance, and outdated library references.
  3. Refactoring for maintainability: Copilot can suggest cleaner logic and structure, add inline comments and docstrings, and break large, complex functions into smaller modular pieces.
  4. Generating documentation: Docstrings for functions, inline comments for complex logic, summaries for nested code blocks — Copilot can produce them all, filling in the gaps left by years of undocumented changes.

Practical techniques for codebase documentation

Understanding and documenting legacy code usually go together. When you need Copilot to explain a block, that’s a good sign the code isn’t readable as-is — and that it needs better documentation. Here are three techniques that work well:

Ask Copilot to explain first

Highlight an unclear code block and ask Copilot to demystify it. Useful prompts include “Explain what this function does,” “Summarize this code for me,” and “What is the purpose of this block?” The concise explanations give you a working understanding of the code’s intent.

Document once you understand

After Copilot clarifies the logic, move on to documentation. You can generate inline comments for specific lines or logic paths, docstrings summarizing function inputs and outputs, and block comments describing larger chunks of code. Be explicit with your prompts — “Add inline comments to explain this code,” “Generate a docstring for this function,” or “Document this block with a summary comment” — and provide as much context as possible. The more context you give, the more accurate the suggestions.

Let comments improve future suggestions

Better-documented code isn’t just easier for humans to read — Copilot also works more effectively with it. Given two similar code blocks, one commented and one not, Copilot generally provides more useful suggestions for the documented version. This creates a positive feedback loop: more comments lead to better Copilot assistance, which leads to clearer code and faster, safer future refactoring.

A workflow for tackling legacy code

The process of documenting and improving legacy code doesn’t have to be overwhelming. A structured, step-by-step approach works well:

  1. Get the big picture. Skim the codebase to map its structure and identify the areas that need the most attention. Ask Copilot questions like “What does this module do?” or “Can you summarize this function?” to quickly orient yourself.
  2. Add function and class summaries. Select a function or class, highlight the code, and ask Copilot to “Write a docstring for this function.” Review and refine the response to ensure it’s accurate.
  3. Clarify tricky logic. For complex sections, use inline comments to explain what’s happening. Ask things like “Explain this loop” or “What’s this condition checking?” and validate the suggestions against the actual code.
  4. Document edge cases. Legacy code often hides assumptions and unusual cases. Ask Copilot “What edge cases does this handle?” and “Explain the assumptions in this logic,” then add comments to capture those details for your team.
  5. Create a high-level README. Use Copilot to write a README section explaining the system’s purpose, architecture, and quirks. Ask “Write a README section for this codebase” and include diagrams or examples where helpful.
  6. Refactor as you go. While documenting, you’ll naturally spot opportunities for cleaner code. Use prompts like “Simplify this function” or “Suggest a better way to handle this loop,” and leave comments or commit messages explaining your changes.
  7. Review and share. Encourage team collaboration on documentation. Copilot makes it easy for multiple people to contribute comments and refine explanations.

By making documentation a routine part of your workflow and letting Copilot handle the heavy lifting, you can bring clarity to even the most tangled legacy codebases. These features are available in the Copilot Free tier, which comes with every personal GitHub account — so there’s no barrier to getting started. The result is code that’s easier for you, your team, and any future developer to understand, extend, and maintain.

From vague guess to documented code: A Python 2 walkthrough

Rather than reach for a language I don't use daily, let's look at a scenario most developers will recognize: untangling a block of Python 2 code in preparation for a migration to Python 3. Here's the starting point:

import urllib2
import cStringIO
import ConfigParser

def fetch_url(url):
    response = urllib2.urlopen(url)
    return response.read()

def parse_ini(content):
    config = ConfigParser.ConfigParser()
    config.readfp(cStringIO.StringIO(content))
    return config

url = 'http://example.com/config.ini'
content = fetch_url(url)
print content

config = parse_ini(content)
print config.sections()

At a glance, I can tell this code is doing something with HTTP requests and INI configuration parsing—but the exact mechanics aren't obvious. That's where GitHub Copilot's /explain slash command comes in. Highlighting the block in my editor, I issued the command with this targeted prompt:

I'm a Python 3 developer. Help me understand this code.

Copilot responded with a concise summary:

This code fetches an INI configuration file from a specified URL and parses its content to extract configuration sections. It then prints the raw content of the INI file and the list of sections found in the parsed configuration.

That clears up the intent. Now that I have a solid understanding, the next step is to document the code inline. I followed up on the conversation with another prompt:

Add comments to document this code.

Copilot returned the same code with clear, section-by-section comments added:

import urllib2  # Library to open and read URLs (Python 2)
import cStringIO  # Library to handle string as file-like objects (Python 2)
import ConfigParser  # Library to parse INI configuration files (Python 2)

def fetch_url(url):
    # Open the URL and read its content
    response = urllib2.urlopen(url)
    return response.read()

def parse_ini(content):
    # Create a ConfigParser object
    config = ConfigParser.ConfigParser()
    # Read the INI content from the string
    config.readfp(cStringIO.StringIO(content))
    return config

# URL of the INI file to fetch
url = 'http://example.com/config.ini'
# Fetch the content of the INI file from the URL
content = fetch_url(url)
# Print the fetched content
print content

# Parse the fetched INI content
config = parse_ini(content)
# Print the sections of the parsed INI configuration
print config.sections()

What took seconds here is significant: I went from a vague sense of what this legacy Python 2 snippet accomplishes to having a documented, shareable version that I—and any teammate—can refer back to. From here, prompting Copilot to port the code to Python 3 would be a natural next step.

Best practices for documenting legacy code with Copilot

Several habits make Copilot especially effective when working through older codebases:

  • Tell Copilot who you are. Context matters. Stating your background—say, "I'm a Python developer"—helps Copilot match its explanations to your experience level.
  • Start with /explain. The slash command is purpose-built for figuring out what legacy or unclear code is doing. It's particularly handy when debugging or before a refactor.
  • Be specific about your goal. Prompts like "modernize this" or "replace deprecated methods" yield far more useful suggestions than a generic request.
  • Iterate. Copilot's responses improve as you engage with them. Refine your request with follow-ups or additional context instead of accepting the first output at face value.

Documentation is a forward-looking investment

Legacy code is rarely pleasant to encounter: it often brings outdated syntax, missing context, and convoluted logic. But a combination of AI assistance and good practices makes it tractable. More importantly, clear documentation isn't purely an exercise in archaeology—it's what keeps a team moving faster later, reduces the risk of mistakes during modernization, and makes the system easier to maintain in the long run. When you find yourself facing down an unfamiliar legacy codebase, treating documentation as an investment rather than a chore is the mindset that pays off.