Blog
September 10, 2025
What happens when you’ve inherited a codebase that’s older than some of your team members, and there’s zero documentation?
Legacy systems are common in established companies, and documentation often falls behind due to tight deadlines and shifting priorities, leading to delays, errors, and poor developer experience.
Here, we’ll explore in more detail why this lack of documentation is a problem, the challenges it creates for developers, and how modern AI-powered tools can help turn complex, undocumented code into clear, visual diagrams for faster onboarding and knowledge sharing.
The Real Cost of Undocumented Legacy Code
When teams change, knowledge walks out the door. Without a source of truth to keep the team on track, onboarding new developers becomes painfully slow, and every bug fix or feature update feels like navigating a maze blindfolded.
According to surveys by Stack Overflow and SonarSource, developers spend only about 32% of their time writing or improving code, while 35% is spent managing existing code. Included in that 35%: understanding and maintaining these undocumented legacy systems.
If nearly half a developer’s day goes to managing old code, a lack of documentation magnifies the cost in delays, errors, and frustration.
The Onboarding Nightmare: A Developer’s Point of View
You can imagine how frustrating a lack of documentation on legacy code must be for a developer, especially one who is unfamiliar with the system.
Let's say a new developer joins your team and they are tasked with updating a payment processing module in a 10-year-old codebase. There are no diagrams or architecture documentation for them to refer to, only sprawling files and cryptic function names.
It might take that person weeks or months just to understand data flows and dependencies, let alone deliver the updated module. Frustration leads to slower delivery and potential errors, and it's more likely they will become dissatisfied in their role.
Why Traditional Documentation Fails for Legacy Code
Traditional documentation practices would require documentation to be manually updated every time there is a change to the codebase.
The problem is that manual docs are never up to date, and diagrams and flowcharts created years ago don’t reflect today’s code. If docs are written, the code has already changed by the time the next person needs them.
Traditional documentation falls out of date so easily because developers avoid writing documentation. Not only do they often find the task time-consuming and boring, but it also takes time away from their other projects and the deliverables that their leaders expect to see.
As a result, documentation is often deprioritized both by developers and leaders, leading to all the issues we’ve explored already.
Enter AI: Automatic Legacy Code Understanding and Diagram Generation
AI makes it possible to avoid both the causes of undocumented code and its consequences. It does this by understanding your code and communicating that understanding in text or visuals.
You can combine multiple AI tools to create a workflow that allows developers to go from code to documentation in only minutes.
For example, after analyzing your existing code, AI can generate:
- Architecture diagrams (services, APIs, modules)
- Data flow diagrams (how data moves through your app)
- Dependency graphs (which modules depend on which)
- UML diagrams (class, sequence, activity)
This all happens automatically, saving hours of manual work. With this documentation, new developers see the big picture instantly, allowing for faster onboarding and fewer mistakes.
Documenting Legacy Code with AI Example: From Code to Confluence in Minutes
This workflow transforms undocumented code into visual, living documentation quickly, making onboarding faster and reducing errors in legacy systems.
Using this framework, you can go from no documentation at all to visually engaging, accurate documentation with minimal effort from the knowledge workers on your team.
Step 1: Use GitHub Copilot to Understand Code
Copilot can scan your codebase and explain complex functions or workflows in natural language or a specified markdown language like Mermaid. For example, you might ask it to summarize the checkout process in a repository. When you do so, Copilot will output a structured description of how those components interact.
Step 2: Visualize Copilot Output with Gliffy
Feed the summary generated by Copilot into Gliffy’s Mermaid diagram feature. This can generate a sequence diagram of the checkout process for immediate review.
Step 3: Publish Your Documentation in Confluence
In just a few clicks, your diagram is available alongside other important product documentation. You can easily edit the diagram without logging into different tools, making it simple to maintain accurate documentation over time.
Final Thoughts
Legacy code doesn’t have to stay a black box. With AI-powered tools like GitHub Copilot and Gliffy, you can make sense of your systems quickly and keep diagrams up to date without manual effort.
Ready to make your code understandable? Try Gliffy free today and start creating diagrams natively in Confluence, saving your team time and ensuring that even the dustiest corners of your code base can get cleaned up quickly.